From bf06c93952ec6cb875e8252e3e64e03b300bc0ba Mon Sep 17 00:00:00 2001 From: JuneChul Roh Date: Wed, 2 Sep 2026 22:30:07 -0500 Subject: [PATCH 01/16] feat(am62d-audioai): add config files and buildable doc skeleton Signed-off-by: JuneChul Roh --- configs/AM62DX/AM62DX_audioai_config.txt | 41 +++++++++++++++++++ configs/AM62DX/AM62DX_audioai_tags.py | 6 +++ configs/AM62DX/AM62DX_audioai_toc.txt | 5 +++ .../audioai/compile_evaluate_inspect.rst | 7 ++++ source/devices/AM62DX/audioai/index.rst | 20 +++++++++ .../devices/AM62DX/audioai/introduction.rst | 7 ++++ source/devices/AM62DX/audioai/model_zoo.rst | 7 ++++ source/devices/AM62DX/audioai/performance.rst | 7 ++++ source/devices/AM62DX/audioai/version.txt | 1 + 9 files changed, 101 insertions(+) create mode 100644 configs/AM62DX/AM62DX_audioai_config.txt create mode 100644 configs/AM62DX/AM62DX_audioai_tags.py create mode 100644 configs/AM62DX/AM62DX_audioai_toc.txt create mode 100644 source/devices/AM62DX/audioai/compile_evaluate_inspect.rst create mode 100644 source/devices/AM62DX/audioai/index.rst create mode 100644 source/devices/AM62DX/audioai/introduction.rst create mode 100644 source/devices/AM62DX/audioai/model_zoo.rst create mode 100644 source/devices/AM62DX/audioai/performance.rst create mode 100644 source/devices/AM62DX/audioai/version.txt diff --git a/configs/AM62DX/AM62DX_audioai_config.txt b/configs/AM62DX/AM62DX_audioai_config.txt new file mode 100644 index 000000000..49404d3c6 --- /dev/null +++ b/configs/AM62DX/AM62DX_audioai_config.txt @@ -0,0 +1,41 @@ +# General family replacement variables and configuration values for AM62D Audio AI build + +# This file is processed by Python scripts to define both replacement +# variable and configuration values for a device family build. +# For replacement variables, all variables should be listed on a single +# line, and all the variables should be listed on consecutive lines +# (i.e. no additional blank lines between the replacement variables). +# Similarly, all the configuration values should be listed on individual, +# consecutive lines (with no blank linues between the configuration values). +# +# Note that neither replacement variables nor configuration values may contain +# a colon (i.e. ":") in their name. However, values may contain colons. + +Replacement Variables +--------------------- +'__PART_FAMILY_NAME__' : 'AM62Dx' +'__PART_FAMILY_DEVICE_NAMES__' : 'AM62Dx' +'__PRODUCT_LINE_NAME__' : 'Sitara MPU' +'__SDK_FULL_NAME__' : 'Processor SDK Linux Audio AI AM62Dx' +'__SDK_SHORT_NAME__' : 'PSDK Audio AI' +'__SDK_DOWNLOAD_URL__' : '`AM62D-SDK-Download-page `__' +'__AUDIOAI_SDK_VERSION__' : '12.1' +'__TIDL_VERSION__' : '11_02_18_00' +'__TIDL_TOOLS_VERSION__' : '11.2.x' +'__MODELZOO_REPO_URL__' : 'https://bitbucket.itg.ti.com/projects/AUDIOAI-ALGO/repos/audioai-modelzoo' +'__TIDLRUNNER_REPO_URL__' : 'https://bitbucket.itg.ti.com/projects/EDGEAI-ALGO/repos/edgeai-tidlrunner' +'__TIDL_TOOLS_REPO_URL__' : 'https://github.com/TexasInstruments/edgeai-tidl-tools' +'__TARGET_VENV_PATH__' : '~/venv/modelzoo' +'__DATASET_AUDIO_CLS__' : 'UrbanSound8K' +'__DATASET_SPEECH_ENH__' : 'VoiceBank-DEMAND-16k' +'__MODEL_ID_VGGISH11__' : 'ac-0101' +'__MODEL_ID_YAMNET__' : 'ac-0201' +'__MODEL_ID_GCRN__' : 'ase-0201' +'__MODEL_ID_GTCRN__' : 'ase-0100' + +Configuration Values +-------------------- +'CONFIG_part_family' : 'AM62DX_family' +'CONFIG_part_variant' : 'AM62DX' +'CONFIG_sdk' : 'SITARA' + diff --git a/configs/AM62DX/AM62DX_audioai_tags.py b/configs/AM62DX/AM62DX_audioai_tags.py new file mode 100644 index 000000000..66d9fecca --- /dev/null +++ b/configs/AM62DX/AM62DX_audioai_tags.py @@ -0,0 +1,6 @@ +# Project name and HTML title +project = u'Audio AI SDK for AM62D' +html_title = 'Audio AI SDK for AM62D Documentation' + +# The master toctree document. +master_doc = 'devices/AM62DX/audioai/index' diff --git a/configs/AM62DX/AM62DX_audioai_toc.txt b/configs/AM62DX/AM62DX_audioai_toc.txt new file mode 100644 index 000000000..eb0fe05ae --- /dev/null +++ b/configs/AM62DX/AM62DX_audioai_toc.txt @@ -0,0 +1,5 @@ +devices/AM62DX/audioai/index +devices/AM62DX/audioai/introduction +devices/AM62DX/audioai/model_zoo +devices/AM62DX/audioai/compile_evaluate_inspect +devices/AM62DX/audioai/performance diff --git a/source/devices/AM62DX/audioai/compile_evaluate_inspect.rst b/source/devices/AM62DX/audioai/compile_evaluate_inspect.rst new file mode 100644 index 000000000..9d575aa1b --- /dev/null +++ b/source/devices/AM62DX/audioai/compile_evaluate_inspect.rst @@ -0,0 +1,7 @@ +.. _audioai-compile-evaluate-inspect: + +############################ +Compile / Evaluate / Inspect +############################ + +Placeholder for the x86 compile, evaluate, and inspect workflow. diff --git a/source/devices/AM62DX/audioai/index.rst b/source/devices/AM62DX/audioai/index.rst new file mode 100644 index 000000000..abd48a647 --- /dev/null +++ b/source/devices/AM62DX/audioai/index.rst @@ -0,0 +1,20 @@ +.. _audioai-index: + +********************************************* +AM62D Audio AI Software Developer's Guide +********************************************* + +.. rubric:: Welcome to the AM62D Audio AI/DL Software Developer's Guide + +.. toctree:: + :maxdepth: 5 + :numbered: + + introduction + model_zoo + compile_evaluate_inspect + performance + ++--------------------------------+--------------------------------------------------------------------------------------------------+ +| .. Image:: /images/E2e.jpg | For technical support please post your questions at `http://e2e.ti.com `__. | ++--------------------------------+--------------------------------------------------------------------------------------------------+ diff --git a/source/devices/AM62DX/audioai/introduction.rst b/source/devices/AM62DX/audioai/introduction.rst new file mode 100644 index 000000000..315e2e504 --- /dev/null +++ b/source/devices/AM62DX/audioai/introduction.rst @@ -0,0 +1,7 @@ +.. _audioai-introduction: + +####################### +Introduction & Overview +####################### + +Placeholder for the AM62D Audio AI introduction and TVM + TIDL framework overview. diff --git a/source/devices/AM62DX/audioai/model_zoo.rst b/source/devices/AM62DX/audioai/model_zoo.rst new file mode 100644 index 000000000..f4aa9e608 --- /dev/null +++ b/source/devices/AM62DX/audioai/model_zoo.rst @@ -0,0 +1,7 @@ +.. _audioai-model-zoo: + +################## +Audio AI Model Zoo +################## + +Placeholder for the AM62D Audio AI Model Zoo on-target demos. diff --git a/source/devices/AM62DX/audioai/performance.rst b/source/devices/AM62DX/audioai/performance.rst new file mode 100644 index 000000000..829344546 --- /dev/null +++ b/source/devices/AM62DX/audioai/performance.rst @@ -0,0 +1,7 @@ +.. _audioai-performance: + +##################### +Performance Benchmark +##################### + +Placeholder for the AM62D Audio AI performance benchmark tables. diff --git a/source/devices/AM62DX/audioai/version.txt b/source/devices/AM62DX/audioai/version.txt new file mode 100644 index 000000000..9df24b162 --- /dev/null +++ b/source/devices/AM62DX/audioai/version.txt @@ -0,0 +1 @@ +12_01_00_00 From f1bd267df1523870f9770359e53b9e6bf3a272dc Mon Sep 17 00:00:00 2001 From: JuneChul Roh Date: Wed, 2 Sep 2026 22:38:34 -0500 Subject: [PATCH 02/16] feat(am62d-audioai): add Section 1 introduction with TVM+TIDL diagram Signed-off-by: JuneChul Roh --- .../devices/AM62DX/audioai/introduction.rst | 99 +++++++++++++++++- source/images/audioai/tvm_tidl_flow.drawio | 86 +++++++++++++++ source/images/audioai/tvm_tidl_flow.png | Bin 0 -> 119800 bytes source/images/audioai/tvm_tidl_flow.svg | 4 + 4 files changed, 188 insertions(+), 1 deletion(-) create mode 100644 source/images/audioai/tvm_tidl_flow.drawio create mode 100644 source/images/audioai/tvm_tidl_flow.png create mode 100644 source/images/audioai/tvm_tidl_flow.svg diff --git a/source/devices/AM62DX/audioai/introduction.rst b/source/devices/AM62DX/audioai/introduction.rst index 315e2e504..a8ba7b25e 100644 --- a/source/devices/AM62DX/audioai/introduction.rst +++ b/source/devices/AM62DX/audioai/introduction.rst @@ -4,4 +4,101 @@ Introduction & Overview ####################### -Placeholder for the AM62D Audio AI introduction and TVM + TIDL framework overview. +.. |tm| unicode:: U+2122 + +This guide documents the audio deep-learning (Audio AI/DL) models supported on +the |__PART_FAMILY_NAME__| platform in |__SDK_FULL_NAME__| |__AUDIOAI_SDK_VERSION__|. +It is written for embedded developers who want to run, compile, evaluate, and +benchmark the supported audio models on the AM62D — either out of the box on the +target with pre-compiled artifacts, or through the x86 compile/evaluate workflow. + +The remaining sections cover: + +- :ref:`Audio AI Model Zoo ` — out-of-box demos on the AM62D + target (inference scripts and Jupyter notebooks) using pre-trained models and + pre-compiled artifacts. +- :ref:`Compile / Evaluate / Inspect ` — the + x86 workflow built on ``edgeai-tidlrunner``: compile each model for the AM62D, + evaluate accuracy on a dataset, and inspect the compiled subgraphs. +- :ref:`Performance benchmark ` — latency and memory + methodology with per-model tables. + +******************** +Supported models +******************** + +Four models are supported, in two task families. Every model is delivered as an +`ONNX `__ graph and, except for GTCRN, is offloaded to the +C7x\ |tm| NPU through the TVM runtime (TVM-RT); GTCRN is the one model that runs +ARM-only. See :ref:`the framework overview ` below for what +that means. + +Audio analysis and classification +================================== + +**VGGish11** (model ID |__MODEL_ID_VGGISH11__|) + Audio-clip classification. Input is a log-mel patch of shape ``(1, 1, 64, 126)``; + output is ``(1, 10)`` class logits plus a ``(1, 512)`` embedding. Runs at 8-bit, + fully offloaded to the C7x\ |tm| NPU via TVM-RT (a single TIDL subgraph). + +**YAMNet** (model ID |__MODEL_ID_YAMNET__|) + Audio-event classification. Input is a log-mel patch of shape ``(1, 1, 96, 64)``; + output is ``(1, 521)`` class scores. Runs at 8-bit, fully offloaded to the + C7x\ |tm| NPU via TVM-RT (a single TIDL subgraph). + +Both classification models are evaluated on the |__DATASET_AUDIO_CLS__| dataset. + +Speech enhancement +================== + +**GCRN** (model ID |__MODEL_ID_GCRN__|) + Convolutional-recurrent speech enhancement (denoising) over a fixed 4-second + window. Input and output are complex spectrograms of shape ``(1, 2, 401, 161)``. + Runs at 16-bit and is offloaded to the C7x\ |tm| NPU via TVM-RT + (``tidl_offload: true``). + +**GTCRN** (model ID |__MODEL_ID_GTCRN__|) + Grouped temporal convolutional-recurrent speech enhancement. Input and output + are complex spectrograms of shape ``(1, 257, T, 2)`` with a dynamic time + dimension ``T``. Runs FP32 on the Arm core through the ONNX runtime + (``tidl_offload: false``) — it is the only supported model that is **not** + offloaded to the C7x\ |tm| NPU. + +Both speech-enhancement models are evaluated on the |__DATASET_SPEECH_ENH__| +dataset. + +.. _audioai-tvm-tidl: + +************************** +TVM + TIDL framework +************************** + +The AM62D audio models are compiled and deployed with **TI TVM**, TI's fork of +the `Apache TVM `__ deep-learning compiler. TI TVM +integrates **TIDL** (TI Deep Learning) as an accelerator backend so that the +layers TIDL supports execute with optimized C7x\ |tm| NPU performance, while any +remaining layers are covered by TVM's own C7x\ |tm| NPU code generation. The +result is a single deployable artifact in which the **entire model runs on the +C7x**\ |tm| **NPU**. + +Compilation runs on an x86 host and proceeds in stages: + +#. **Model import** — the ONNX graph is imported into TVM. +#. **Partitioning** — using the TIDL import API, the graph is split into + TIDL-supported subgraphs (compiled by the TIDL network compiler) and the + remaining operators (handled by TVM C7x\ |tm| NPU code generation). +#. **Artifact generation** — both parts are packaged into one TVM-RT deployable + artifact (``deploy_lib.so`` + ``deploy_graph.json`` + ``deploy_param.params``). + +At run time on the AM62D, the TVM runtime loads the artifact and dispatches the +offloaded subgraphs to TIDL-RT on the C7x\ |tm| NPU. + +.. figure:: ../../../images/audioai/tvm_tidl_flow.png + :scale: 55 % + :align: center + + TVM + TIDL compile-and-deploy flow for the AM62D audio models. Green blocks + are handled by TIDL; white blocks are handled by TVM. VGGish11, YAMNet, and + GCRN follow this offloaded path; GTCRN runs ARM-only on the ONNX runtime. + +.. TODO(WI-002): confirm the TVM + TIDL diagram concept with Anand Pathak before release. diff --git a/source/images/audioai/tvm_tidl_flow.drawio b/source/images/audioai/tvm_tidl_flow.drawio new file mode 100644 index 000000000..83b434ea0 --- /dev/null +++ b/source/images/audioai/tvm_tidl_flow.drawio @@ -0,0 +1,86 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/source/images/audioai/tvm_tidl_flow.png b/source/images/audioai/tvm_tidl_flow.png new file mode 100644 index 0000000000000000000000000000000000000000..6b5406f2f7682b7413a9265fe6c6e274d654066a GIT binary patch literal 119800 zcmdSBWn5M5+bz1(2UHYPQjoA{0V(M)09n%A-7P7N0SE{v-3<#^gmi}@D2r}sP(oUy zBt6&k`M+oH_k7wP_Bp?O)|Y2JxN6RM-{Tr%T;rNS3UX3}1XKhl6pB##@gpS^3O@{m z!b?1N8vgS9d|xCA_0Jm*6%A+GXHt%a4i>WZCZ;wh)U%|u1YM`hd!zxrgxupH>wd2| zYqmcZ)p4)zlt~Zl=4bj{TqX#*;<&m!;`YzSbFYl~o>G^FI$ha9KX#y{NpAT-lw>;Y 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ONNX audio model
(YAMNet, VGGish11, GCRN)
Model import into TVM
Partitioning
TIDL import API
TVM C7x™ DSP
code generation
TIDL network compiler
TVM-RT deployable artifact
(deploy_lib.so + graph.json + params)
compile time (x86)
run time (AM62D)
TVM runtime
TIDL-RT
Arm® core
C7x™ NPU
TIDL unsupported ops
TIDL supported ops
Text is not SVG - cannot display \ No newline at end of file +
ONNX audio model
Model import into TVM
Partitioning
TIDL import API
TVM C7™ DSP
code generation
TIDL network compiler
TVM-RT deployable artifact
(deploy_lib.so + graph.json + params)
compile time (x86)
run time (AM62D)
TVM runtime
TIDL-RT
C7™ NPU
TIDL unsupported ops
TIDL supported ops
Text is not SVG - cannot display \ No newline at end of file From c0b64716835d9f49a88dd77b22272f7803003502 Mon Sep 17 00:00:00 2001 From: JuneChul Roh Date: Thu, 10 Sep 2026 22:37:40 -0500 Subject: [PATCH 08/16] docs(am62d-audioai): reconcile compile/eval/inspect with standalone AM62D setup Signed-off-by: JuneChul Roh --- .../audioai/compile_evaluate_inspect.rst | 153 +++++++----------- 1 file changed, 61 insertions(+), 92 deletions(-) diff --git a/source/devices/AM62DX/audioai/compile_evaluate_inspect.rst b/source/devices/AM62DX/audioai/compile_evaluate_inspect.rst index b2a3de826..6556d933a 100644 --- a/source/devices/AM62DX/audioai/compile_evaluate_inspect.rst +++ b/source/devices/AM62DX/audioai/compile_evaluate_inspect.rst @@ -53,13 +53,16 @@ All commands are run from the repository root (``edgeai-tidlrunner/``) and take ``--config_path `` pointing at the per-model YAML. The same config file is reused across ``compile``, ``infer``, and ``evaluate``. +.. _audioai-am62d-setup: + ******************** Setup ******************** -Model compilation runs on an x86 PC (Ubuntu Linux recommended). The TIDL tools -used for compilation target Python 3.10, so create a dedicated Python 3.10 -environment. These steps assume `pyenv `__. +Model compilation runs on an x86 PC (Ubuntu Linux recommended). The AM62D audio +flow is self-contained: a single setup script installs the whole TVM toolchain +into a dedicated Python 3.10 environment. The TIDL tools target Python 3.10. +These steps assume `pyenv `__. **1. Clone the repository** (|__TIDLRUNNER_REPO_URL__|): @@ -68,36 +71,62 @@ environment. These steps assume `pyenv `__. $ git clone https://github.com/TexasInstruments/edgeai-tidlrunner.git $ cd edgeai-tidlrunner -**2. Create and activate the Python 3.10 environment.** +**2. Create and activate a dedicated Python 3.10 environment.** The AM62D flow +uses a release-candidate TVM wheel; keep it in its own venv (``tidlrunner-am62d``) +so it never clobbers a standard ``tidlrunner`` setup: .. code-block:: console $ pyenv install 3.10 - $ pyenv virtualenv 3.10 tidlrunner - $ pyenv activate tidlrunner + $ pyenv virtualenv 3.10 tidlrunner-am62d + $ pyenv activate tidlrunner-am62d -**3. Run the PC setup script.** This downloads the TIDL tools into -``tools/tidl_tools_package/`` and installs the runner: +**3. Run the AM62D setup script.** With the venv active, one script installs +everything this flow needs: .. code-block:: console - $ ./setup_runner_pc.sh + $ ./devices/setup_am62d.sh -The TIDL tools version is pinned by ``TIDL_TOOLS_VERSION`` inside -``setup_runner_pc.sh`` and can be overridden on the command line. **The tools -version used to compile must match the version on the target** — artifacts -compiled for a different TIDL version will not run: +It downloads the ARM GCC 15.2 and C7000 CGT 5.0.0.LTS cross-toolchains into +``tools/tidl_tools_package/bin/`` (skipped if already present), installs the +release-candidate x86 TVM wheel — which bundles the AM62D x86 TIDL tools *inside* +the package — and installs ``tidlrunner[pc,audio]``, ``tools``, ``onnxruntime``, +and ``tidl_onnx_model_optimizer``. The ``[audio]`` extra pulls in the audio +packages (``librosa``, ``soundfile``, ``scipy``, ``pesq``, ``pystoi``, +``scikit-learn``). No separate ``pip install`` is needed. + +**4. Source the env script once per shell.** TVM ``dlopen``\ s the TIDL runtime +(``libvx_tidl_rt.so``) before Python's own import runs, so the tool paths must be +live in the shell *before* ``tidlrunner-cli`` starts. Source — do **not** execute +— ``devices/am62d_env.sh`` once; every ``tidlrunner-cli`` command run afterward in +that shell picks the vars up: .. code-block:: console - $ TIDL_TOOLS_VERSION="11.2.x" ./setup_runner_pc.sh + $ source devices/am62d_env.sh -**4. Install the audio extras.** The audio pipelines need extra Python packages -(``librosa``, ``soundfile``, ``scipy``, ``pesq``, ``pystoi``, ``scikit-learn``): +It exports: -.. code-block:: console +.. list-table:: + :header-rows: 1 + :widths: 26 74 - $ pip install -e "tidlrunner[audio]" + * - Variable + - Value + * - ``TIDL_TOOLS_PATH`` + - ``/3rdparty/x86_tidl_tools/AM62D`` (resolved from the + active venv's installed ``tvm``) + * - ``LD_LIBRARY_PATH`` + - prepends ``TIDL_TOOLS_PATH`` so ``libvx_tidl_rt.so`` resolves + * - ``ARM64_GCC_PATH`` + - the aarch64 GNU toolchain installed by ``setup_am62d.sh`` under + ``tools/tidl_tools_package/bin/`` (override by exporting before sourcing) + * - ``CGT7X_ROOT`` + - the C7000 code-generation tools installed by ``setup_am62d.sh`` under + ``tools/tidl_tools_package/bin/`` (override by exporting before sourcing) + * - ``SOC`` + - ``am62d`` Datasets ======== @@ -120,76 +149,22 @@ The classification configs use UrbanSound8K **fold 10** (837 samples) as the test set; the speech-enhancement configs use the VoiceBank-DEMAND-16k test set (824 files). -.. _audioai-am62d-interim-setup: - -Interim AM62D TVM-RT compile path -================================= - -.. warning:: - - The AM62D C7\ |tm| NPU compile path for the three TVM-RT models (VGGish11, - YAMNet, GCRN) is currently an **interim feasibility flow**, not the - productized ``setup_runner_pc.sh`` / ``TIDL_TOOLS_VERSION`` download. It is - documented here for reproducibility and will be replaced before release. - -The three TVM-RT models compile through a **dedicated environment** -(``tidlrunner-am62d``) that has the release-candidate TVM wheel installed. That -wheel ships the x86 AM62D TIDL tools *inside* the package, so the AM62D compile -uses the wheel-bundled tools rather than the ones ``setup_runner_pc.sh`` -downloads. TVM ``dlopen``\ s the TIDL runtime before Python's own import runs, -so the tool paths must be exported into the environment *before* ``tidlrunner-cli`` -starts. A sourceable env script, ``devices/am62d_env.sh``, exports these into -the current shell once; every ``tidlrunner-cli`` command run afterward in that -shell picks them up. It exports: - -.. list-table:: - :header-rows: 1 - :widths: 26 74 - - * - Variable - - Value - * - ``TIDL_TOOLS_PATH`` - - ``/3rdparty/x86_tidl_tools/AM62D`` (resolved from the - active venv's installed ``tvm``) - * - ``LD_LIBRARY_PATH`` - - prepends ``TIDL_TOOLS_PATH`` so ``libvx_tidl_rt.so`` resolves - * - ``ARM64_GCC_PATH`` - - path to your aarch64 GNU toolchain (e.g. ````) - * - ``CGT7X_ROOT`` - - path to your C7000 code-generation tools (e.g. ````) - * - ``SOC`` - - ``am62d`` - -Activate the dedicated environment, then source the env script once per shell -before running ``tidlrunner-cli`` directly: +The reference ONNX models auto-download at compile time via ``.link`` files, so +fetching them ahead of time is optional. To pre-download all four: .. code-block:: console - $ pyenv activate tidlrunner-am62d - $ source devices/am62d_env.sh - $ tidlrunner-cli compile --config_path - -.. note:: - - GTCRN does **not** need this env script. It runs ARM-only through ONNX - Runtime (``tidl_offload: false``) and compiles under the standard - ``tidlrunner`` environment from steps 1–4. - -.. TODO: replace the interim ``devices/am62d_env.sh`` env script + - ``tidlrunner-am62d`` venv with the productized ``setup_runner_pc.sh`` / - ``TIDL_TOOLS_VERSION`` AM62D flow before release. Confirm the final tools - version string (artifact folder 11_02_18_00 vs. RC bundled tools 11.02.16.00). + $ bash examples/audio/scripts/download_audio_models.sh ******************** Compile and evaluate ******************** -Each model has one config that drives all stages. Three models compile to the -C7\ |tm| NPU through the TVM runtime; GTCRN is the ONNX-RT exception that runs -ARM-only. The three TVM-RT models below assume the AM62D env from -`Interim AM62D TVM-RT compile path`_ — ``pyenv activate tidlrunner-am62d`` -followed by ``source devices/am62d_env.sh`` — is already sourced in the -current shell. The final AM62D config paths are: +Each model has one config that drives all stages. All four run under the +``tidlrunner-am62d`` environment with ``devices/am62d_env.sh`` already sourced +(see `Setup`_). Three models compile to the C7\ |tm| NPU through the TVM runtime; +GTCRN runs ARM-only through ONNX Runtime (no C7\ |tm| NPU offload) but uses the +same environment. The AM62D config paths are: .. list-table:: :header-rows: 1 @@ -265,20 +240,15 @@ GTCRN (ONNX-RT, ARM-only) GTCRN has a dynamic time axis and TIDL-unsupported operators, so it runs on the Arm Cortex-A cores through ONNX Runtime (``tidl_offload: false``) — no C7\ |tm| NPU -offload and **no** AM62D wrapper. Run it directly under the standard -``tidlrunner`` environment: +offload. It uses the same ``tidlrunner-am62d`` environment as the other models; +it simply does not offload to the DSP: .. code-block:: console - $ pyenv activate tidlrunner $ CFG=data/configs/samples/models/audio/speech_enhancement/voicebank_demand_16k/gtcrn_dns3_config.yaml - $ tidlrunner-cli compile --config_path $CFG --target_device AM62D - $ tidlrunner-cli infer --config_path $CFG --target_device AM62D - $ tidlrunner-cli evaluate --config_path $CFG --target_device AM62D - -.. note:: - - + $ tidlrunner-cli compile --config_path $CFG + $ tidlrunner-cli infer --config_path $CFG + $ tidlrunner-cli evaluate --config_path $CFG ******************** Model Inspector @@ -317,8 +287,7 @@ Because GCRN now compiles with ``tidl_offload: true`` on AM62D, its inspector report shows a **real C7x DSP subgraph** rather than an all-Arm fallback: the Subgraphs tab lists a single offloaded subgraph (16-bit) with its layer mapping, and the Performance tab breaks that subgraph down by cycles and memory. -Generate it with the AM62D env sourced (see -`Interim AM62D TVM-RT compile path`_): +Generate it with the AM62D env sourced (see `Setup`_): .. code-block:: console From 319271f89fe10b1ecbe3a0945824bedfed2a9f1f Mon Sep 17 00:00:00 2001 From: JuneChul Roh Date: Tue, 22 Sep 2026 18:43:21 -0500 Subject: [PATCH 09/16] docs(am62d-audioai): strip prompt symbol from copy button output Audio AI docs prefix every shell command with "$ "; configure copybutton to strip it so copied text is directly runnable. Signed-off-by: JuneChul Roh --- conf.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/conf.py b/conf.py index c84b2b8c9..12d9e51b8 100644 --- a/conf.py +++ b/conf.py @@ -261,6 +261,12 @@ FAMILY = os.environ.get("DEVFAMILY", "") OS = os.environ.get("OS", "") + +# Audio AI docs prefix every shell command with a "$ " prompt symbol; strip it +# from the copy-button output so copied text is directly runnable. +if OS == "audioai": + copybutton_prompt_text = "$ " + try: globals().update(importlib.import_module(f"configs.{FAMILY}.{FAMILY}_{OS}_tags").__dict__) except ModuleNotFoundError as exc: From db96350171b1a3f48958109130ef22afe8d165bc Mon Sep 17 00:00:00 2001 From: JuneChul Roh Date: Tue, 22 Sep 2026 19:11:07 -0500 Subject: [PATCH 10/16] docs(am62d-audioai): update Introduction and Model Zoo sections Signed-off-by: JuneChul Roh --- .../devices/AM62DX/audioai/introduction.rst | 5 +- source/devices/AM62DX/audioai/model_zoo.rst | 113 ++++++++++++------ 2 files changed, 76 insertions(+), 42 deletions(-) diff --git a/source/devices/AM62DX/audioai/introduction.rst b/source/devices/AM62DX/audioai/introduction.rst index 2fb7c3fe1..839b1b9d1 100644 --- a/source/devices/AM62DX/audioai/introduction.rst +++ b/source/devices/AM62DX/audioai/introduction.rst @@ -125,7 +125,4 @@ offloaded subgraphs to TIDL-RT on the C7\ |tm| NPU. :align: center TVM + TIDL compile-and-deploy flow for the AM62D audio models. Green blocks - are handled by TIDL; white blocks are handled by TVM. VGGish11, YAMNet, and - GCRN follow this offloaded path; GTCRN runs ARM-only on the ONNX runtime. - -.. TODO(WI-002): confirm the TVM + TIDL diagram concept with Anand Pathak before release. + are handled by TIDL; white blocks are handled by TVM. diff --git a/source/devices/AM62DX/audioai/model_zoo.rst b/source/devices/AM62DX/audioai/model_zoo.rst index 401a589c5..db41f0963 100644 --- a/source/devices/AM62DX/audioai/model_zoo.rst +++ b/source/devices/AM62DX/audioai/model_zoo.rst @@ -53,12 +53,12 @@ What ships - Speech enhancement (|__DATASET_SPEECH_ENH__|) - |__MODEL_ID_GTCRN__| - Arm core (ONNX runtime, FP32) - - Notebook only + - Script + notebook -VGGish11, YAMNet, and GCRN each ship a pre-compiled artifact and run offloaded -to the C7\ |tm| NPU through the TVM runtime. GTCRN ships **no** compiled -artifact — it runs FP32 on the Arm core through the ONNX runtime and is -demonstrated in a notebook only. +VGGish11, YAMNet, and GCRN each ship a pre-compiled TVM-RT + TIDL artifact and +run offloaded to the C7\ |tm| NPU. GTCRN ships **no** compiled artifact — it +runs FP32 on the Arm core through the ONNX runtime, packaged as a plain ONNX +Runtime artifact. ******************** On-target setup @@ -66,7 +66,7 @@ On-target setup The steps below run on the AM62D target (aarch64), on the Linux command line of the Processor SDK rootfs. The notebooks and scripts run in a Python virtual -environment that reuses the TIDL-enabled TVM/ONNX runtime already present in the +environment that reuses the TIDL-enabled TVM runtime already present in the rootfs. **1. Clone the repository.** Clone the AudioAI ModelZoo repository @@ -78,35 +78,27 @@ rootfs. $ git clone https://github.com/TexasInstruments-Sandbox/audioai-modelzoo.git $ cd audioai-modelzoo -**2. Download the ONNX models.** Downloads the four model graphs into -``models/onnx/``: +**2. Download the model artifacts.** Downloads the three pre-compiled +TVM-RT + TIDL artifacts (VGGish11, YAMNet, GCRN) plus the GTCRN ONNX Runtime +artifact into ``model_artifacts//am62d/``: .. code-block:: console $ ./download_models.sh -y -**3. Download the compiled artifacts.** Downloads the three pre-compiled -TVM-RT + TIDL artifacts into ``model_artifacts/11_02_18_00/am62d/`` -(GTCRN has no artifact): - -.. code-block:: console - - $ ./download_artifacts.sh -y - -Both download scripts default to an interactive selection menu; the ``-y`` flag +The script defaults to an interactive selection menu; the ``-y`` flag downloads everything non-interactively. Add ``-l`` to print the source URLs without downloading. -**4. Create the virtual environment.** Creates the venv at +**3. Create the virtual environment.** Creates the venv at |__TARGET_VENV_PATH__| with ``--system-site-packages`` so the TIDL-enabled -runtime from the rootfs is visible, then verifies that -``TIDLExecutionProvider`` is available: +runtime from the rootfs is visible: .. code-block:: console - $ ./venv/setup_venv.sh + $ ./setup_venv.sh -**5. Activate the venv.** Activate it in every new shell before running the +**4. Activate the venv.** Activate it in every new shell before running the demos: .. code-block:: console @@ -117,9 +109,8 @@ demos: Inference script demos ********************** -Run each script from its own model directory in the activated venv. Three of -the four models ship a command-line script; GTCRN is demonstrated in a notebook -only (see :ref:`Jupyter notebook demos `). +Run each script from its own model directory in the activated venv. All four +models ship a command-line script. YAMNet (sound classification) ============================= @@ -203,7 +194,7 @@ the destination wav. Expected output (abridged): .. code-block:: text - Machine: aarch64 SOC: am62d TIDL_VER: 11_02_18_00 + Machine: aarch64 SOC: am62d TIDL_VER: ... Loading TVM + TIDL session... Running inference on noisy.wav (9.77s)... Wrote enhanced audio to sample_wav/enhanced.wav @@ -216,19 +207,54 @@ the destination wav. Expected output (abridged): Wrote benchmark report to sample_wav/gcrn-benchmark.md +GTCRN (speech enhancement) +========================== + +.. code-block:: console + + $ cd ~/tidl/audioai-modelzoo/inference/gtcrn_se + $ python3 gtcrn_infer_audio.py --input sample_wav/mix.wav + +Unlike the other three models, GTCRN has no TVM-RT + TIDL artifact — the script +loads the ONNX graph directly and runs it on ``CPUExecutionProvider`` (Arm +core, FP32). It writes the enhanced audio to ``enh_onnx.wav`` beside the input; +pass ``--output`` to change the destination wav. Expected output (abridged): + +.. code-block:: text + + Machine: aarch64 + Model: .../ase-0100_onnxrt_speech_enhancement_voicebank_demand_16k_gtcrn_dns3_onnx/model/gtcrn_dns3.onnx + Loading ONNX Runtime session... + Running inference on mix.wav (9.77s)... + Wrote enhanced audio to sample_wav/enh_onnx.wav + + GTCRN ONNX Runtime (CPU) benchmark (9.77s audio) + + block total (ms) + ------------------------ ---------- + pre-processing (STFT) ... + model inference ... + post-processing (iSTFT) ... + end-to-end ... + + Real-Time Factor (inference / audio) = 0.0700 + +GTCRN is already faster than real time on the Arm core, so it needs no NPU +offload. + .. _audioai-model-zoo-notebooks: ********************** Jupyter notebook demos ********************** -All four reference models — including GTCRN — have a Jupyter notebook demo. Start -Jupyter Lab from the activated venv: +All four reference models have a Jupyter notebook demo. Start Jupyter Lab from +the activated venv: .. code-block:: console $ cd ~/tidl/audioai-modelzoo - $ ./venv/jupyter_lab_venv.sh + $ ./jupyter_lab_venv.sh The launcher prints a highlighted access URL and pre-loads the four inference notebooks in tabs. Open the URL in a browser on a machine that can reach the @@ -259,19 +285,30 @@ The notebooks are: Compiled artifacts ******************** -``download_artifacts.sh`` places the pre-compiled TVM-RT + TIDL artifacts under -``model_artifacts/11_02_18_00/am62d/``: +``download_models.sh`` places the artifacts under +``model_artifacts//am62d/``. Each model gets its own directory, +named after its model ID, containing a ``model/`` folder (the trained ONNX +model) and, where applicable, an ``artifacts/`` folder (the compiled model +artifacts). VGGish11, YAMNet, and GCRN each get a pre-compiled TVM-RT + TIDL +artifact: - |__MODEL_ID_VGGISH11__| — ``ac-0101_tvmrt_audio_classification_urbansound8k_vggish11_onnx`` (VGGish11) - |__MODEL_ID_YAMNET__| — ``ac-0201_tvmrt_audio_classification_urbansound8k_yamnet_onnx`` (YAMNet) - |__MODEL_ID_GCRN__| — ``ase-0201_tvmrt_speech_enhancement_voicebank_demand_16k_gcrn_fixed_4sec_onnx`` (GCRN) -Each artifact directory holds the TVM-RT deployables (``deploy_lib.so``, -``deploy_graph.json``, ``deploy_param.params``) in both target (``.evm``, -aarch64) and host (``.pc``, x86) variants. The demo scripts and notebooks -select the ``.evm`` variant automatically on the target. +Each of those artifact directories' ``artifacts/`` folder holds the TVM-RT +deployables (``deploy_lib.so``, ``deploy_graph.json``, ``deploy_param.params``) +in both target (``.evm``, aarch64) and host (``.pc``, x86) variants. The demo +scripts and notebooks select the ``.evm`` variant automatically on the target. + +GTCRN (|__MODEL_ID_GTCRN__|) gets an ONNX Runtime artifact instead of a +compiled one: + +- |__MODEL_ID_GTCRN__| — ``ase-0100_onnxrt_speech_enhancement_voicebank_demand_16k_gtcrn_dns3_onnx`` (GTCRN) -GTCRN (|__MODEL_ID_GTCRN__|) ships **no** compiled artifact: it runs FP32 on the -Arm core through the ONNX runtime directly from its ONNX graph. These artifacts -are produced by the x86 workflow in +Its directory holds only the ``model/`` folder, with the ``gtcrn_dns3.onnx`` +graph — there is no ``artifacts/`` folder since no TIDL compilation is +involved. The demo script and notebook load the ONNX model directly with +``CPUExecutionProvider`` and run FP32 on the Arm core. Compiled TVM-RT + TIDL +artifacts for your own models are produced by the x86 workflow in :ref:`Compile / Evaluate / Inspect `. From 630cb1c9af82615a780310d3f2c0b63673f04fbe Mon Sep 17 00:00:00 2001 From: JuneChul Roh Date: Wed, 23 Sep 2026 12:17:44 -0500 Subject: [PATCH 11/16] docs(am62d-audioai): Updated "Model Compile / Evaluate / Inspect" section. Signed-off-by: JuneChul Roh --- .../AM62DX/audioai/compile_evaluate_inspect.rst | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/source/devices/AM62DX/audioai/compile_evaluate_inspect.rst b/source/devices/AM62DX/audioai/compile_evaluate_inspect.rst index 6556d933a..fa8ed0088 100644 --- a/source/devices/AM62DX/audioai/compile_evaluate_inspect.rst +++ b/source/devices/AM62DX/audioai/compile_evaluate_inspect.rst @@ -14,7 +14,7 @@ dataset — using **edgeai-tidlrunner**. It runs entirely on an Ubuntu x86 PC; nothing here touches the target. This is the workflow for *any* audio model, not just the reference models. The -four reference models below are worked examples; to bring your own model you +four reference models below are worked examples. To bring your own model you point the same ``tidlrunner-cli`` at your ONNX graph and a per-model YAML config, using a reference config as a starting template. Which operators offload to the C7\ |tm| NPU (vs. fall back to the Arm core) is determined by TIDL — see @@ -72,7 +72,7 @@ These steps assume `pyenv `__. $ cd edgeai-tidlrunner **2. Create and activate a dedicated Python 3.10 environment.** The AM62D flow -uses a release-candidate TVM wheel; keep it in its own venv (``tidlrunner-am62d``) +uses a TI TVM wheel; keep it in its own venv (``tidlrunner-am62d``) so it never clobbers a standard ``tidlrunner`` setup: .. code-block:: console @@ -90,7 +90,7 @@ everything this flow needs: It downloads the ARM GCC 15.2 and C7000 CGT 5.0.0.LTS cross-toolchains into ``tools/tidl_tools_package/bin/`` (skipped if already present), installs the -release-candidate x86 TVM wheel — which bundles the AM62D x86 TIDL tools *inside* +x86 TI TVM wheel — which bundles the AM62D x86 TIDL tools *inside* the package — and installs ``tidlrunner[pc,audio]``, ``tools``, ``onnxruntime``, and ``tidl_onnx_model_optimizer``. The ``[audio]`` extra pulls in the audio packages (``librosa``, ``soundfile``, ``scipy``, ``pesq``, ``pystoi``, @@ -146,11 +146,11 @@ sets. Download them from the repository root: They land under ``data/datasets/UrbanSound8K/`` and ``data/datasets/VoiceBank-DEMAND-16k/`` — the paths the model configs expect. The classification configs use UrbanSound8K **fold 10** (837 samples) as the -test set; the speech-enhancement configs use the VoiceBank-DEMAND-16k test set +test set; the speech-enhancement configs use the VoiceBank-DEMAND-16k **test** set (824 files). The reference ONNX models auto-download at compile time via ``.link`` files, so -fetching them ahead of time is optional. To pre-download all four: +fetching them ahead of time is **optional**. To pre-download all reference ONNX models: .. code-block:: console @@ -168,12 +168,12 @@ same environment. The AM62D config paths are: .. list-table:: :header-rows: 1 - :widths: 14 10 12 10 54 + :widths: 12 10 12 18 54 * - Model - Model ID - Runtime - - Bits + - Tensor Bits - Config path (relative to ``edgeai-tidlrunner/``) * - VGGish11 - |__MODEL_ID_VGGISH11__| @@ -188,7 +188,7 @@ same environment. The AM62D config paths are: * - GCRN - |__MODEL_ID_GCRN__| - TVM-RT - - 16 + - 16 (LSTM), FP32 (Enc/Dec) - ``data/configs/samples/models/audio/speech_enhancement/voicebank_demand_16k/gcrn_fixed_4sec_tvmrt_config.yaml`` * - GTCRN - |__MODEL_ID_GTCRN__| From 6e9c9ad60b353dca5b8b65f09277a4e9c48ff9a5 Mon Sep 17 00:00:00 2001 From: JuneChul Roh Date: Thu, 8 Oct 2026 22:31:51 -0500 Subject: [PATCH 12/16] docs(am62d-audioai): switch build target to OS=edgeai and update model zoo URL Rename source/devices/AM62DX/audioai to edgeai and the AM62DX_audioai_* config files to AM62DX_edgeai_*. Point __MODELZOO_REPO_URL__ and the model zoo clone command to TexasInstruments/audioai-modelzoo. Signed-off-by: JuneChul Roh --- conf.py | 2 +- configs/AM62DX/AM62DX_audioai_toc.txt | 5 ----- .../{AM62DX_audioai_config.txt => AM62DX_edgeai_config.txt} | 2 +- .../AM62DX/{AM62DX_audioai_tags.py => AM62DX_edgeai_tags.py} | 2 +- configs/AM62DX/AM62DX_edgeai_toc.txt | 5 +++++ .../AM62DX/{audioai => edgeai}/compile_evaluate_inspect.rst | 0 source/devices/AM62DX/{audioai => edgeai}/index.rst | 0 source/devices/AM62DX/{audioai => edgeai}/introduction.rst | 0 source/devices/AM62DX/{audioai => edgeai}/model_zoo.rst | 2 +- source/devices/AM62DX/{audioai => edgeai}/performance.rst | 0 source/devices/AM62DX/{audioai => edgeai}/version.txt | 0 11 files changed, 9 insertions(+), 9 deletions(-) delete mode 100644 configs/AM62DX/AM62DX_audioai_toc.txt rename configs/AM62DX/{AM62DX_audioai_config.txt => AM62DX_edgeai_config.txt} (98%) rename configs/AM62DX/{AM62DX_audioai_tags.py => AM62DX_edgeai_tags.py} (76%) create mode 100644 configs/AM62DX/AM62DX_edgeai_toc.txt rename source/devices/AM62DX/{audioai => edgeai}/compile_evaluate_inspect.rst (100%) rename source/devices/AM62DX/{audioai => edgeai}/index.rst (100%) rename source/devices/AM62DX/{audioai => edgeai}/introduction.rst (100%) rename source/devices/AM62DX/{audioai => edgeai}/model_zoo.rst (99%) rename source/devices/AM62DX/{audioai => edgeai}/performance.rst (100%) rename source/devices/AM62DX/{audioai => edgeai}/version.txt (100%) diff --git a/conf.py b/conf.py index 12d9e51b8..5aa8837cf 100644 --- a/conf.py +++ b/conf.py @@ -264,7 +264,7 @@ # Audio AI docs prefix every shell command with a "$ " prompt symbol; strip it # from the copy-button output so copied text is directly runnable. -if OS == "audioai": +if OS == "edgeai" and FAMILY == "AM62DX": copybutton_prompt_text = "$ " try: diff --git a/configs/AM62DX/AM62DX_audioai_toc.txt b/configs/AM62DX/AM62DX_audioai_toc.txt deleted file mode 100644 index eb0fe05ae..000000000 --- a/configs/AM62DX/AM62DX_audioai_toc.txt +++ /dev/null @@ -1,5 +0,0 @@ -devices/AM62DX/audioai/index -devices/AM62DX/audioai/introduction -devices/AM62DX/audioai/model_zoo -devices/AM62DX/audioai/compile_evaluate_inspect -devices/AM62DX/audioai/performance diff --git a/configs/AM62DX/AM62DX_audioai_config.txt b/configs/AM62DX/AM62DX_edgeai_config.txt similarity index 98% rename from configs/AM62DX/AM62DX_audioai_config.txt rename to configs/AM62DX/AM62DX_edgeai_config.txt index fe538eb3d..c243a452e 100644 --- a/configs/AM62DX/AM62DX_audioai_config.txt +++ b/configs/AM62DX/AM62DX_edgeai_config.txt @@ -22,7 +22,7 @@ Replacement Variables '__AUDIOAI_SDK_VERSION__' : '12.1' '__TIDL_VERSION__' : '11_02_18_00' '__TIDL_TOOLS_VERSION__' : '11.2.x' -'__MODELZOO_REPO_URL__' : 'https://github.com/TexasInstruments-Sandbox/audioai-modelzoo' +'__MODELZOO_REPO_URL__' : 'https://github.com/TexasInstruments/audioai-modelzoo' '__TIDLRUNNER_REPO_URL__' : 'https://github.com/TexasInstruments/edgeai-tidlrunner' '__TIDL_TOOLS_REPO_URL__' : 'https://github.com/TexasInstruments/edgeai-tidl-tools' '__TARGET_VENV_PATH__' : '~/venv/modelzoo' diff --git a/configs/AM62DX/AM62DX_audioai_tags.py b/configs/AM62DX/AM62DX_edgeai_tags.py similarity index 76% rename from configs/AM62DX/AM62DX_audioai_tags.py rename to configs/AM62DX/AM62DX_edgeai_tags.py index 4e85bcd38..a6627eba2 100644 --- a/configs/AM62DX/AM62DX_audioai_tags.py +++ b/configs/AM62DX/AM62DX_edgeai_tags.py @@ -3,4 +3,4 @@ html_title = 'Audio AI for AM62D Documentation' # The master toctree document. -master_doc = 'devices/AM62DX/audioai/index' +master_doc = 'devices/AM62DX/edgeai/index' diff --git a/configs/AM62DX/AM62DX_edgeai_toc.txt b/configs/AM62DX/AM62DX_edgeai_toc.txt new file mode 100644 index 000000000..d69093766 --- /dev/null +++ b/configs/AM62DX/AM62DX_edgeai_toc.txt @@ -0,0 +1,5 @@ +devices/AM62DX/edgeai/index +devices/AM62DX/edgeai/introduction +devices/AM62DX/edgeai/model_zoo +devices/AM62DX/edgeai/compile_evaluate_inspect +devices/AM62DX/edgeai/performance diff --git a/source/devices/AM62DX/audioai/compile_evaluate_inspect.rst b/source/devices/AM62DX/edgeai/compile_evaluate_inspect.rst similarity index 100% rename from source/devices/AM62DX/audioai/compile_evaluate_inspect.rst rename to source/devices/AM62DX/edgeai/compile_evaluate_inspect.rst diff --git a/source/devices/AM62DX/audioai/index.rst b/source/devices/AM62DX/edgeai/index.rst similarity index 100% rename from source/devices/AM62DX/audioai/index.rst rename to source/devices/AM62DX/edgeai/index.rst diff --git a/source/devices/AM62DX/audioai/introduction.rst b/source/devices/AM62DX/edgeai/introduction.rst similarity index 100% rename from source/devices/AM62DX/audioai/introduction.rst rename to source/devices/AM62DX/edgeai/introduction.rst diff --git a/source/devices/AM62DX/audioai/model_zoo.rst b/source/devices/AM62DX/edgeai/model_zoo.rst similarity index 99% rename from source/devices/AM62DX/audioai/model_zoo.rst rename to source/devices/AM62DX/edgeai/model_zoo.rst index db41f0963..bef874248 100644 --- a/source/devices/AM62DX/audioai/model_zoo.rst +++ b/source/devices/AM62DX/edgeai/model_zoo.rst @@ -75,7 +75,7 @@ rootfs. .. code-block:: console $ mkdir -p ~/tidl && cd ~/tidl - $ git clone https://github.com/TexasInstruments-Sandbox/audioai-modelzoo.git + $ git clone https://github.com/TexasInstruments/audioai-modelzoo.git $ cd audioai-modelzoo **2. Download the model artifacts.** Downloads the three pre-compiled diff --git a/source/devices/AM62DX/audioai/performance.rst b/source/devices/AM62DX/edgeai/performance.rst similarity index 100% rename from source/devices/AM62DX/audioai/performance.rst rename to source/devices/AM62DX/edgeai/performance.rst diff --git a/source/devices/AM62DX/audioai/version.txt b/source/devices/AM62DX/edgeai/version.txt similarity index 100% rename from source/devices/AM62DX/audioai/version.txt rename to source/devices/AM62DX/edgeai/version.txt From 0d748a16fe99d43da6ab43e8a8fa261a86bfe9e0 Mon Sep 17 00:00:00 2001 From: JuneChul Roh Date: Thu, 8 Oct 2026 22:44:26 -0500 Subject: [PATCH 13/16] docs(am62d-audioai): split Compile/Evaluate/Inspect into two sections Split into "Model Compile and Evaluate" (model_compile_evaluate.rst) and "Model Inspect" (model_inspect.rst). Update the toctree, the AM62DX edgeai toc config, labels, and cross-references in the other pages. Signed-off-by: JuneChul Roh --- configs/AM62DX/AM62DX_edgeai_toc.txt | 3 +- source/devices/AM62DX/edgeai/index.rst | 3 +- source/devices/AM62DX/edgeai/introduction.rst | 15 +++-- ...inspect.rst => model_compile_evaluate.rst} | 61 ++----------------- .../devices/AM62DX/edgeai/model_inspect.rst | 61 +++++++++++++++++++ source/devices/AM62DX/edgeai/model_zoo.rst | 4 +- source/devices/AM62DX/edgeai/performance.rst | 4 +- 7 files changed, 83 insertions(+), 68 deletions(-) rename source/devices/AM62DX/edgeai/{compile_evaluate_inspect.rst => model_compile_evaluate.rst} (78%) create mode 100644 source/devices/AM62DX/edgeai/model_inspect.rst diff --git a/configs/AM62DX/AM62DX_edgeai_toc.txt b/configs/AM62DX/AM62DX_edgeai_toc.txt index d69093766..0faa09f81 100644 --- a/configs/AM62DX/AM62DX_edgeai_toc.txt +++ b/configs/AM62DX/AM62DX_edgeai_toc.txt @@ -1,5 +1,6 @@ devices/AM62DX/edgeai/index devices/AM62DX/edgeai/introduction devices/AM62DX/edgeai/model_zoo -devices/AM62DX/edgeai/compile_evaluate_inspect +devices/AM62DX/edgeai/model_compile_evaluate +devices/AM62DX/edgeai/model_inspect devices/AM62DX/edgeai/performance diff --git a/source/devices/AM62DX/edgeai/index.rst b/source/devices/AM62DX/edgeai/index.rst index abd48a647..590e47335 100644 --- a/source/devices/AM62DX/edgeai/index.rst +++ b/source/devices/AM62DX/edgeai/index.rst @@ -12,7 +12,8 @@ AM62D Audio AI Software Developer's Guide introduction model_zoo - compile_evaluate_inspect + model_compile_evaluate + model_inspect performance +--------------------------------+--------------------------------------------------------------------------------------------------+ diff --git a/source/devices/AM62DX/edgeai/introduction.rst b/source/devices/AM62DX/edgeai/introduction.rst index 839b1b9d1..74681433c 100644 --- a/source/devices/AM62DX/edgeai/introduction.rst +++ b/source/devices/AM62DX/edgeai/introduction.rst @@ -23,9 +23,11 @@ The remaining sections cover: - :ref:`Audio AI Model Zoo ` — out-of-box demos on the AM62D target (inference scripts and Jupyter notebooks) using pre-trained models and pre-compiled artifacts. -- :ref:`Compile / Evaluate / Inspect ` — the - x86 BYOM workflow built on ``edgeai-tidlrunner``: compile a model for the AM62D, - evaluate accuracy on a dataset, and inspect the compiled subgraphs. +- :ref:`Model Compile and Evaluate ` — the + x86 BYOM workflow built on ``edgeai-tidlrunner``: compile a model for the AM62D + and evaluate accuracy on a dataset. +- :ref:`Model Inspect ` — analyze the compiled subgraphs + and per-layer behavior with Model Inspector. - :ref:`Performance benchmark ` — latency and memory methodology with per-model tables. @@ -37,10 +39,11 @@ The end-to-end path is the same for a reference model or your own model: #. **Obtain an ONNX model** — a reference graph shipped with the AudioAI ModelZoo, or your own exported ONNX. -#. **Compile** it for the C7\ |tm| NPU on an x86 host (:ref:`Compile / Evaluate / - Inspect `). +#. **Compile** it for the C7\ |tm| NPU on an x86 host (:ref:`Model Compile and + Evaluate `). #. **Evaluate** accuracy or audio-quality metrics on a dataset. -#. **Inspect** the compiled subgraphs and per-layer analysis. +#. **Inspect** the compiled subgraphs and per-layer analysis (:ref:`Model + Inspect `). #. **Run** on the AM62D target using the compiled artifact (:ref:`Audio AI Model Zoo `). #. **Benchmark** latency and memory (:ref:`Performance `). diff --git a/source/devices/AM62DX/edgeai/compile_evaluate_inspect.rst b/source/devices/AM62DX/edgeai/model_compile_evaluate.rst similarity index 78% rename from source/devices/AM62DX/edgeai/compile_evaluate_inspect.rst rename to source/devices/AM62DX/edgeai/model_compile_evaluate.rst index fa8ed0088..aca9420c4 100644 --- a/source/devices/AM62DX/edgeai/compile_evaluate_inspect.rst +++ b/source/devices/AM62DX/edgeai/model_compile_evaluate.rst @@ -1,8 +1,8 @@ -.. _audioai-compile-evaluate-inspect: +.. _audioai-compile-evaluate: -################################## -Model Compile / Evaluate / Inspect -################################## +########################### +Model Compile and Evaluate +########################### .. |tm| unicode:: U+2122 @@ -47,7 +47,7 @@ CLI — ``tidlrunner-cli`` — exposes the stages this section uses: speech-enhancement metrics. * - ``inspect`` - One-shot compile + inference + reference comparison that emits an - interactive HTML analysis report (see `Model Inspector`_). + interactive HTML analysis report (see :ref:`Model Inspect `). All commands are run from the repository root (``edgeai-tidlrunner/``) and take ``--config_path `` pointing at the per-model YAML. The same config file is @@ -249,54 +249,3 @@ it simply does not offload to the DSP: $ tidlrunner-cli compile --config_path $CFG $ tidlrunner-cli infer --config_path $CFG $ tidlrunner-cli evaluate --config_path $CFG - -******************** -Model Inspector -******************** - -``tidlrunner-cli inspect`` is a one-shot analysis pipeline: it compiles the -model for TIDL, runs inference, re-runs a non-TIDL reference for comparison, -computes per-layer signal-to-noise ratios, and emits a **self-contained -interactive HTML report** — no need to run ``compile`` and ``infer`` -separately. The report opens in any browser and lands at: - -.. code-block:: text - - work_dirs/compile////inspector/modelinspector.html - -The report has three tabs: - -- **Model Summary** — acceleration ratio (layers on the C7x DSP vs. Arm - fallback), input/output specs, and an interactive ONNX graph. -- **Subgraphs** — the TIDL subgraph(s) offloaded to the DSP, with per-layer - identity and a quantization analysis (INT8 vs. FP32 activation histograms and - scatter plots). -- **Performance** — *estimated* per-layer processing time, DSP cycle breakdown - (total / IO / kernel), memory usage (DDR / L2 / MSMC), and SNR metrics. - -.. note:: - - All Performance-tab numbers are **estimates** from the compiler, not - on-target measurements. Model Inspector requires TIDL tools **11.02 or - later** (earlier versions emit SVG subgraph files it cannot read). - -GCRN showcase — an offloaded DSP subgraph -========================================= - -Because GCRN now compiles with ``tidl_offload: true`` on AM62D, its inspector -report shows a **real C7x DSP subgraph** rather than an all-Arm fallback: -the Subgraphs tab lists a single offloaded subgraph (16-bit) with its layer -mapping, and the Performance tab breaks that subgraph down by cycles and memory. -Generate it with the AM62D env sourced (see `Setup`_): - -.. code-block:: console - - $ CFG=data/configs/samples/models/audio/speech_enhancement/voicebank_demand_16k/gcrn_fixed_4sec_tvmrt_config.yaml - $ tidlrunner-cli inspect --config_path $CFG - -For contrast, running ``inspect`` on the GTCRN (ONNX-RT) config produces a -report with **no** DSP subgraph — every layer is Arm fallback — which is what a -non-offloaded model looks like in the inspector. GTCRN also runs with -``tidl_offload: false``, so its work directory uses a ``notidl`` segment in -place of the ```` shown in the path template above, e.g. -``work_dirs/compile/AM62D/notidl//...``. diff --git a/source/devices/AM62DX/edgeai/model_inspect.rst b/source/devices/AM62DX/edgeai/model_inspect.rst new file mode 100644 index 000000000..e7ae6b914 --- /dev/null +++ b/source/devices/AM62DX/edgeai/model_inspect.rst @@ -0,0 +1,61 @@ +.. _audioai-model-inspect: + +############# +Model Inspect +############# + +Model Inspector analyzes a model on the x86 host after (or instead of) the +:ref:`compile and evaluate ` flow. It uses the same +edgeai-tidlrunner installation and environment described in +:ref:`Setup `. + +******************** +Model Inspector +******************** + +``tidlrunner-cli inspect`` is a one-shot analysis pipeline: it compiles the +model for TIDL, runs inference, re-runs a non-TIDL reference for comparison, +computes per-layer signal-to-noise ratios, and emits a **self-contained +interactive HTML report** — no need to run ``compile`` and ``infer`` +separately. The report opens in any browser and lands at: + +.. code-block:: text + + work_dirs/compile////inspector/modelinspector.html + +The report has three tabs: + +- **Model Summary** — acceleration ratio (layers on the C7x DSP vs. Arm + fallback), input/output specs, and an interactive ONNX graph. +- **Subgraphs** — the TIDL subgraph(s) offloaded to the DSP, with per-layer + identity and a quantization analysis (INT8 vs. FP32 activation histograms and + scatter plots). +- **Performance** — *estimated* per-layer processing time, DSP cycle breakdown + (total / IO / kernel), memory usage (DDR / L2 / MSMC), and SNR metrics. + +.. note:: + + All Performance-tab numbers are **estimates** from the compiler, not + on-target measurements. Model Inspector requires TIDL tools **11.02 or + later** (earlier versions emit SVG subgraph files it cannot read). + +GCRN showcase — an offloaded DSP subgraph +========================================= + +Because GCRN now compiles with ``tidl_offload: true`` on AM62D, its inspector +report shows a **real C7x DSP subgraph** rather than an all-Arm fallback: +the Subgraphs tab lists a single offloaded subgraph (16-bit) with its layer +mapping, and the Performance tab breaks that subgraph down by cycles and memory. +Generate it with the AM62D env sourced (see :ref:`Setup `): + +.. code-block:: console + + $ CFG=data/configs/samples/models/audio/speech_enhancement/voicebank_demand_16k/gcrn_fixed_4sec_tvmrt_config.yaml + $ tidlrunner-cli inspect --config_path $CFG + +For contrast, running ``inspect`` on the GTCRN (ONNX-RT) config produces a +report with **no** DSP subgraph — every layer is Arm fallback — which is what a +non-offloaded model looks like in the inspector. GTCRN also runs with +``tidl_offload: false``, so its work directory uses a ``notidl`` segment in +place of the ```` shown in the path template above, e.g. +``work_dirs/compile/AM62D/notidl//...``. diff --git a/source/devices/AM62DX/edgeai/model_zoo.rst b/source/devices/AM62DX/edgeai/model_zoo.rst index bef874248..b15ca952b 100644 --- a/source/devices/AM62DX/edgeai/model_zoo.rst +++ b/source/devices/AM62DX/edgeai/model_zoo.rst @@ -14,7 +14,7 @@ compiling anything yourself. Two demo front-ends are provided: standalone Python inference scripts and Jupyter notebooks. The same on-target runtime hosts your own compiled artifacts once you build them through the x86 flow. This section runs entirely **on the target**; the x86 compile/evaluate flow is covered -in :ref:`Compile / Evaluate / Inspect `. +in :ref:`Model Compile and Evaluate `. .. note:: @@ -311,4 +311,4 @@ graph — there is no ``artifacts/`` folder since no TIDL compilation is involved. The demo script and notebook load the ONNX model directly with ``CPUExecutionProvider`` and run FP32 on the Arm core. Compiled TVM-RT + TIDL artifacts for your own models are produced by the x86 workflow in -:ref:`Compile / Evaluate / Inspect `. +:ref:`Model Compile and Evaluate `. diff --git a/source/devices/AM62DX/edgeai/performance.rst b/source/devices/AM62DX/edgeai/performance.rst index 5df57681b..39a518d1c 100644 --- a/source/devices/AM62DX/edgeai/performance.rst +++ b/source/devices/AM62DX/edgeai/performance.rst @@ -11,7 +11,7 @@ reference |__PART_FAMILY_NAME__| Audio AI models. The same methodology (perfsim estimate + on-target measured) applies to any model you compile through the workflow. It is **not** an accuracy or audio-quality benchmark: model quality (classification accuracy, PESQ / STOI / SI-SDR) is measured by the ``evaluate`` -step in :ref:`Compile / Evaluate / Inspect `. +step in :ref:`Model Compile and Evaluate `. *********** Methodology @@ -133,4 +133,4 @@ Scope & Limitations This section does **not** assess model quality. Classification accuracy and speech-enhancement quality metrics are produced by ``tidlrunner-cli evaluate`` and documented in -:ref:`Compile / Evaluate / Inspect `. +:ref:`Model Compile and Evaluate `. From 95ea7ac35fa1dd7555e6b1269d661c1258087b1b Mon Sep 17 00:00:00 2001 From: JuneChul Roh Date: Mon, 12 Oct 2026 13:50:24 -0500 Subject: [PATCH 14/16] docs(am62d-audioai): fix inspector path and Jupyter launcher description Signed-off-by: JuneChul Roh --- configs/AM62DX/AM62DX_edgeai_config.txt | 3 +-- source/devices/AM62DX/edgeai/model_inspect.rst | 8 ++++++-- source/devices/AM62DX/edgeai/model_zoo.rst | 8 ++++---- 3 files changed, 11 insertions(+), 8 deletions(-) diff --git a/configs/AM62DX/AM62DX_edgeai_config.txt b/configs/AM62DX/AM62DX_edgeai_config.txt index c243a452e..03cf3901c 100644 --- a/configs/AM62DX/AM62DX_edgeai_config.txt +++ b/configs/AM62DX/AM62DX_edgeai_config.txt @@ -6,7 +6,7 @@ # line, and all the variables should be listed on consecutive lines # (i.e. no additional blank lines between the replacement variables). # Similarly, all the configuration values should be listed on individual, -# consecutive lines (with no blank linues between the configuration values). +# consecutive lines (with no blank lines between the configuration values). # # Note that neither replacement variables nor configuration values may contain # a colon (i.e. ":") in their name. However, values may contain colons. @@ -38,4 +38,3 @@ Configuration Values 'CONFIG_part_family' : 'AM62DX_family' 'CONFIG_part_variant' : 'AM62DX' 'CONFIG_sdk' : 'SITARA' - diff --git a/source/devices/AM62DX/edgeai/model_inspect.rst b/source/devices/AM62DX/edgeai/model_inspect.rst index e7ae6b914..08d69f982 100644 --- a/source/devices/AM62DX/edgeai/model_inspect.rst +++ b/source/devices/AM62DX/edgeai/model_inspect.rst @@ -21,7 +21,11 @@ separately. The report opens in any browser and lands at: .. code-block:: text - work_dirs/compile////inspector/modelinspector.html + work_dirs/compile////inspect/modelinspector.html + +```` is the model's work directory, named after its model ID, +runtime, task, dataset, and model, e.g. +``ase-0201_tvmrt_speech_enhancement_voicebank_demand_16k_gcrn_fixed_4sec_onnx``. The report has three tabs: @@ -58,4 +62,4 @@ report with **no** DSP subgraph — every layer is Arm fallback — which is wha non-offloaded model looks like in the inspector. GTCRN also runs with ``tidl_offload: false``, so its work directory uses a ``notidl`` segment in place of the ```` shown in the path template above, e.g. -``work_dirs/compile/AM62D/notidl//...``. +``work_dirs/compile/AM62D/notidl//...``. diff --git a/source/devices/AM62DX/edgeai/model_zoo.rst b/source/devices/AM62DX/edgeai/model_zoo.rst index b15ca952b..e452a694f 100644 --- a/source/devices/AM62DX/edgeai/model_zoo.rst +++ b/source/devices/AM62DX/edgeai/model_zoo.rst @@ -256,15 +256,15 @@ the activated venv: $ cd ~/tidl/audioai-modelzoo $ ./jupyter_lab_venv.sh -The launcher prints a highlighted access URL and pre-loads the four inference -notebooks in tabs. Open the URL in a browser on a machine that can reach the -target and enter the token ``tidl`` when prompted: +The launcher prints a highlighted access URL. Open the URL in a browser on a +machine that can reach the target and enter the token ``tidl`` when prompted: .. code-block:: text Access URL: http://:8888/lab?token=tidl -The notebooks are: +Jupyter Lab opens with the GCRN notebook loaded. To run a different model, pick +its notebook from the file browser in the left sidebar. The notebooks are: .. list-table:: :header-rows: 1 From 880ba369df9ac89651e67306f301cc62340a0a82 Mon Sep 17 00:00:00 2001 From: JuneChul Roh Date: Tue, 13 Oct 2026 18:44:51 -0500 Subject: [PATCH 15/16] chore(am62d-audioai): stop tracking diagram sources and export script Signed-off-by: JuneChul Roh --- source/images/audioai/export-diagrams.sh | 18 ----- source/images/audioai/tvm_tidl_flow.drawio | 83 --------------------- source/images/audioai/tvm_tidl_flow.png | Bin 43683 -> 0 bytes 3 files changed, 101 deletions(-) delete mode 100755 source/images/audioai/export-diagrams.sh delete mode 100644 source/images/audioai/tvm_tidl_flow.drawio delete mode 100644 source/images/audioai/tvm_tidl_flow.png diff --git a/source/images/audioai/export-diagrams.sh b/source/images/audioai/export-diagrams.sh deleted file mode 100755 index 4638f5f65..000000000 --- a/source/images/audioai/export-diagrams.sh +++ /dev/null @@ -1,18 +0,0 @@ -#!/usr/bin/env bash -# Re-export every .drawio source in this directory to .png and .svg. -# Run after editing any .drawio file here. -# -# --svg-theme light is required: drawio's default (auto) emits CSS -# light-dark()/color-scheme, which makes the diagram invert colors -# under a dark OS/browser theme. Sphinx has no dark-mode support, so -# the exported SVG must stay pinned to the light palette. -set -euo pipefail -cd "$(dirname "$0")" - -DRAWIO=$(command -v drawio || echo /opt/drawio/drawio) - -for src in *.drawio; do - base="${src%.drawio}" - "$DRAWIO" --export --format png --embed-diagram --output "${base}.png" "$src" - "$DRAWIO" --export --format svg --svg-theme light --embed-diagram --output "${base}.svg" "$src" -done diff --git a/source/images/audioai/tvm_tidl_flow.drawio b/source/images/audioai/tvm_tidl_flow.drawio deleted file mode 100644 index 6f99ee07a..000000000 --- a/source/images/audioai/tvm_tidl_flow.drawio +++ /dev/null @@ -1,83 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/source/images/audioai/tvm_tidl_flow.png b/source/images/audioai/tvm_tidl_flow.png deleted file mode 100644 index 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zLs{5FpYw$=((|6{)RXdtaOA3_*qu*VG4XNc7SPhS+GPj7T5Hw$Le;E980je)jZyC} z5-Tj*zg?wSbW}q*x8Io)*kBHMtvY0g&N1N-i5xb(&s2%Uwco(yn z8;`9MqDr+>ZSL{t9kr)Q>}2|`%{H-7M zGP*)uK~ntBb~Z+%739~3g;|~7Pi@vmfolA3!JUsH<3GRKp)&#@;{9o&PHEpyKQ@g0 zAGql}QkhZk9Y97Ddzb{#ssOKIQ22)}E5yqRUR(I8mCr5~0!sZ@dlX6^!A|V7ZT}V% zG8Yd|FY(xZEM%k$lc6rUj_W*W%)`2;Sw1kkp}`M=oC(~&@q<6K4LN#4Z_?wC2`+~W zFYYjAEfECq=?h$pHdCxTBa)oF4I`!fsAb50n$ic9xZ;8%$MbpesKll_uSB@x?>Z;P z1L>Tm4_^%OS=d0^mse-U8YG{OWnZ2u2A~Y|);>*#~p5O{ZPFh*2NJ8KHKLCeB5Ay&3 From 0f63803cd39357d5d124f8fdcb0a0d819e86a82c Mon Sep 17 00:00:00 2001 From: JuneChul Roh Date: Tue, 13 Oct 2026 18:44:51 -0500 Subject: [PATCH 16/16] docs(am62d-audioai): address PR review feedback on edgeai pages Trim the Vale vocabulary to the entries Vale needs, fix heading adornments and non-ASCII text, use isonum.txt and #. autonumbered steps, and replace the removed replacement variables with literal values. Signed-off-by: JuneChul Roh --- .../config/vocabularies/PSDK/accept.txt | 18 +-- .rstcheck.cfg | 2 +- configs/AM62DX/AM62DX_edgeai_config.txt | 10 -- source/devices/AM62DX/edgeai/index.rst | 4 +- source/devices/AM62DX/edgeai/introduction.rst | 68 ++++---- .../AM62DX/edgeai/model_compile_evaluate.rst | 146 +++++++++--------- .../devices/AM62DX/edgeai/model_inspect.rst | 16 +- source/devices/AM62DX/edgeai/model_zoo.rst | 106 ++++++------- source/devices/AM62DX/edgeai/performance.rst | 34 ++-- 9 files changed, 190 insertions(+), 214 deletions(-) diff --git a/.github/styles/config/vocabularies/PSDK/accept.txt b/.github/styles/config/vocabularies/PSDK/accept.txt index ac18fcc3b..ec68dd68d 100644 --- a/.github/styles/config/vocabularies/PSDK/accept.txt +++ b/.github/styles/config/vocabularies/PSDK/accept.txt @@ -1,17 +1,10 @@ Armbian -BYOM BeagleBone Broadcom -C7x -DDR Debian -GCRN -GMACs GStreamer -GTCRN Jacinto Khronos -NPU NXP ONNX OpenCL @@ -19,20 +12,15 @@ OpenGL OpenVX PVRCarbon PVRTune -RTF Sitara Skia Slint TFLite -TIDL TVM Trixie UniFlash -VGGish11 -VoiceBank Vulkan Weston -YAMNet Yocto Zink [Bb]ackplane @@ -45,15 +33,13 @@ Zink [Mm]ulticast [Rr]enderers? [Ss]carthgap +[Ss]ubgraphs? [Tt]oolchain -aarch64 balenaEtcher bdebstrap -edgeai fdisk +logits? mmdebstrap perfsim -pyenv -tidlrunner umount venv diff --git a/.rstcheck.cfg b/.rstcheck.cfg index 3c3e4dfd8..e867f2893 100644 --- a/.rstcheck.cfg +++ b/.rstcheck.cfg @@ -3,5 +3,5 @@ ignore_directives = ifconfig,graphviz,grid,grid-item,grid-item-card,card,dropdow # ignore the following languages because they require external compilers and # snippets in this repo depend on external code ignore_languages = c,cpp,c++ -ignore_substitutions = __PART_FAMILY_NAME__,__PART_FAMILY_DEVICE_NAMES__,__PRODUCT_LINE_NAME__,__SDK_BUILD_MACHINE__,__SDK_FULL_NAME__,__SDK_SHORT_NAME__,__SDK_INSTALL_FILE__,__SDK_INSTALL_DIR__,__SDK_DOWNLOAD_URL__,__LINUX_UBUNTU_VERSION_LONG__,__LINUX_UBUNTU_VERSION_SHORT__,__OPTEE_PLATFORM_FLAVOR__,__RTOS_UBUNTU_VERSION_LONG__,__WINDOWS_SUPPORTED_LONG__,__FEATURINGMATRIX__,__SYSFW_CORE_NAME__,__PCIE_BASE_ADDRESS__,__PCIE_DEVICE_ID__,__IMAGE_TYPE__,__SDK_OS_NAME__ +ignore_substitutions = __PART_FAMILY_NAME__,__PART_FAMILY_DEVICE_NAMES__,__PRODUCT_LINE_NAME__,__SDK_BUILD_MACHINE__,__SDK_FULL_NAME__,__SDK_SHORT_NAME__,__SDK_INSTALL_FILE__,__SDK_INSTALL_DIR__,__SDK_DOWNLOAD_URL__,__LINUX_UBUNTU_VERSION_LONG__,__LINUX_UBUNTU_VERSION_SHORT__,__OPTEE_PLATFORM_FLAVOR__,__RTOS_UBUNTU_VERSION_LONG__,__WINDOWS_SUPPORTED_LONG__,__FEATURINGMATRIX__,__SYSFW_CORE_NAME__,__PCIE_BASE_ADDRESS__,__PCIE_DEVICE_ID__,__IMAGE_TYPE__,__SDK_OS_NAME__,__AUDIOAI_SDK_VERSION__,trade ignore_messages = (Hyperlink target.*is not referenced\.) diff --git a/configs/AM62DX/AM62DX_edgeai_config.txt b/configs/AM62DX/AM62DX_edgeai_config.txt index 03cf3901c..9520fbd15 100644 --- a/configs/AM62DX/AM62DX_edgeai_config.txt +++ b/configs/AM62DX/AM62DX_edgeai_config.txt @@ -22,16 +22,6 @@ Replacement Variables '__AUDIOAI_SDK_VERSION__' : '12.1' '__TIDL_VERSION__' : '11_02_18_00' '__TIDL_TOOLS_VERSION__' : '11.2.x' -'__MODELZOO_REPO_URL__' : 'https://github.com/TexasInstruments/audioai-modelzoo' -'__TIDLRUNNER_REPO_URL__' : 'https://github.com/TexasInstruments/edgeai-tidlrunner' -'__TIDL_TOOLS_REPO_URL__' : 'https://github.com/TexasInstruments/edgeai-tidl-tools' -'__TARGET_VENV_PATH__' : '~/venv/modelzoo' -'__DATASET_AUDIO_CLS__' : 'UrbanSound8K' -'__DATASET_SPEECH_ENH__' : 'VoiceBank-DEMAND-16k' -'__MODEL_ID_VGGISH11__' : 'ac-0101' -'__MODEL_ID_YAMNET__' : 'ac-0201' -'__MODEL_ID_GCRN__' : 'ase-0201' -'__MODEL_ID_GTCRN__' : 'ase-0100' Configuration Values -------------------- diff --git a/source/devices/AM62DX/edgeai/index.rst b/source/devices/AM62DX/edgeai/index.rst index 590e47335..1617e815e 100644 --- a/source/devices/AM62DX/edgeai/index.rst +++ b/source/devices/AM62DX/edgeai/index.rst @@ -1,8 +1,8 @@ .. _audioai-index: -********************************************* +######################################### AM62D Audio AI Software Developer's Guide -********************************************* +######################################### .. rubric:: Welcome to the AM62D Audio AI/DL Software Developer's Guide diff --git a/source/devices/AM62DX/edgeai/introduction.rst b/source/devices/AM62DX/edgeai/introduction.rst index 74681433c..ec75cc579 100644 --- a/source/devices/AM62DX/edgeai/introduction.rst +++ b/source/devices/AM62DX/edgeai/introduction.rst @@ -1,16 +1,16 @@ +.. include:: + .. _audioai-introduction: ####################### Introduction & Overview ####################### -.. |tm| unicode:: U+2122 - This guide walks through the workflow for developing and deploying audio deep-learning (Audio AI/DL) models on the |__PART_FAMILY_NAME__| platform, using |__SDK_FULL_NAME__| |__AUDIOAI_SDK_VERSION__|. It is written for embedded developers who want to run, compile, evaluate, and benchmark audio models on the -AM62D — either out of the box on the target with pre-compiled artifacts, or +AM62D - either out of the box on the target with pre-compiled artifacts, or through the x86 compile/evaluate workflow. Four **reference models** serve as worked examples throughout the guide, but the @@ -20,15 +20,15 @@ bring-your-own-model (BYOM) flow that accepts any supported `ONNX The remaining sections cover: -- :ref:`Audio AI Model Zoo ` — out-of-box demos on the AM62D +- :ref:`Audio AI Model Zoo ` - out-of-box demos on the AM62D target (inference scripts and Jupyter notebooks) using pre-trained models and pre-compiled artifacts. -- :ref:`Model Compile and Evaluate ` — the +- :ref:`Model Compile and Evaluate ` - the x86 BYOM workflow built on ``edgeai-tidlrunner``: compile a model for the AM62D and evaluate accuracy on a dataset. -- :ref:`Model Inspect ` — analyze the compiled subgraphs +- :ref:`Model Inspect ` - analyze the compiled subgraphs and per-layer behavior with Model Inspector. -- :ref:`Performance benchmark ` — latency and memory +- :ref:`Performance benchmark ` - latency and memory methodology with per-model tables. ******************** @@ -37,9 +37,9 @@ Development workflow The end-to-end path is the same for a reference model or your own model: -#. **Obtain an ONNX model** — a reference graph shipped with the AudioAI ModelZoo, +#. **Obtain an ONNX model** - a reference graph shipped with the AudioAI ModelZoo, or your own exported ONNX. -#. **Compile** it for the C7\ |tm| NPU on an x86 host (:ref:`Model Compile and +#. **Compile** it for the C7\ |trade| NPU on an x86 host (:ref:`Model Compile and Evaluate `). #. **Evaluate** accuracy or audio-quality metrics on a dataset. #. **Inspect** the compiled subgraphs and per-layer analysis (:ref:`Model @@ -48,80 +48,80 @@ The end-to-end path is the same for a reference model or your own model: (:ref:`Audio AI Model Zoo `). #. **Benchmark** latency and memory (:ref:`Performance `). -******************** +**************** Reference models -******************** +**************** Four reference models are provided across two task families, as worked examples of the workflow. Each is delivered as an `ONNX `__ graph and, -except for GTCRN, is offloaded to the C7\ |tm| NPU through the TVM runtime +except for GTCRN, is offloaded to the C7\ |trade| NPU through the TVM runtime (TVM-RT); GTCRN is the one that runs ARM-only. See :ref:`the framework overview ` below for what that means. The same flow accepts your own ONNX model, and more reference models will be added over time. Audio analysis and classification -================================== +================================= -**VGGish11** (model ID |__MODEL_ID_VGGISH11__|) +**VGGish11** (model ID ``ac-0101``) Audio-clip classification. Input is a log-mel patch of shape ``(1, 1, 64, 126)``; output is ``(1, 10)`` class logits plus a ``(1, 512)`` embedding. Runs at 8-bit, - fully offloaded to the C7\ |tm| NPU via TVM-RT (a single TIDL subgraph). + fully offloaded to the C7\ |trade| NPU via TVM-RT (a single TIDL subgraph). -**YAMNet** (model ID |__MODEL_ID_YAMNET__|) +**YAMNet** (model ID ``ac-0201``) Audio-event classification. Input is a log-mel patch of shape ``(1, 1, 96, 64)``; output is ``(1, 521)`` class scores. Runs at 8-bit, fully offloaded to the - C7\ |tm| NPU via TVM-RT (a single TIDL subgraph). + C7\ |trade| NPU via TVM-RT (a single TIDL subgraph). -Both classification models are evaluated on the |__DATASET_AUDIO_CLS__| dataset. +Both classification models are evaluated on the UrbanSound8K dataset. Speech enhancement ================== -**GCRN** (model ID |__MODEL_ID_GCRN__|) - Convolutional-recurrent speech enhancement (denoising) over a fixed 4-second +**GCRN** (model ID ``ase-0201``) + Convolutional-recurrent speech enhancement (noise suppression) over a fixed 4-second window. Input and output are complex spectrograms of shape ``(1, 2, 401, 161)``. - Runs at 16-bit and is offloaded to the C7\ |tm| NPU via TVM-RT + Runs at 16-bit and is offloaded to the C7\ |trade| NPU via TVM-RT (``tidl_offload: true``). -**GTCRN** (model ID |__MODEL_ID_GTCRN__|) +**GTCRN** (model ID ``ase-0100``) Grouped temporal convolutional-recurrent speech enhancement. Input and output are complex spectrograms of shape ``(1, 257, T, 2)`` with a dynamic time dimension ``T``. Runs FP32 on the Arm core through the ONNX runtime - (``tidl_offload: false``) — it is the only supported model that is **not** - offloaded to the C7\ |tm| NPU. + (``tidl_offload: false``) - it is the only supported model that is **not** + offloaded to the C7\ |trade| NPU. -Both speech-enhancement models are evaluated on the |__DATASET_SPEECH_ENH__| +Both speech-enhancement models are evaluated on the VoiceBank-DEMAND-16k dataset. .. _audioai-tvm-tidl: -************************** +******************** TVM + TIDL framework -************************** +******************** The AM62D audio models are compiled and deployed with **TI TVM**, TI's fork of the `Apache TVM `__ deep-learning compiler. The target -accelerator is the **C7**\ |tm| **NPU** — TI's Neural Processing Unit, which +accelerator is the **C7**\ |trade| **NPU** - TI's Neural Processing Unit, which combines the **C7x DSP** with the **Matrix Multiplication Accelerator (MMA)** (referred to as *C7x+MMA* in older documentation). TI TVM integrates **TIDL** (TI Deep Learning) as an accelerator backend: TIDL-supported layers are offloaded to the MMA, while TVM generates **C7x DSP** code for any remaining layers. Because the MMA and the C7x DSP together make up -the C7\ |tm| NPU, the result is a single deployable artifact in which the -**entire model runs on the C7**\ |tm| **NPU**. +the C7\ |trade| NPU, the result is a single deployable artifact in which the +**entire model runs on the C7**\ |trade| **NPU**. Compilation runs on an x86 host and proceeds in stages: -#. **Model import** — the ONNX graph is imported into TVM. -#. **Partitioning** — using the TIDL import API, the graph is split into +#. **Model import** - the ONNX graph is imported into TVM. +#. **Partitioning** - using the TIDL import API, the graph is split into TIDL-supported subgraphs (compiled by the TIDL network compiler) and the remaining operators (handled by TVM C7x DSP code generation). -#. **Artifact generation** — both parts are packaged into one TVM-RT deployable +#. **Artifact generation** - both parts are packaged into one TVM-RT deployable artifact (``deploy_lib.so`` + ``deploy_graph.json`` + ``deploy_param.params``). At run time on the AM62D, the TVM runtime loads the artifact and dispatches the -offloaded subgraphs to TIDL-RT on the C7\ |tm| NPU. +offloaded subgraphs to TIDL-RT on the C7\ |trade| NPU. .. figure:: ../../../images/audioai/tvm_tidl_flow.svg :width: 80 % diff --git a/source/devices/AM62DX/edgeai/model_compile_evaluate.rst b/source/devices/AM62DX/edgeai/model_compile_evaluate.rst index aca9420c4..d3a04f26c 100644 --- a/source/devices/AM62DX/edgeai/model_compile_evaluate.rst +++ b/source/devices/AM62DX/edgeai/model_compile_evaluate.rst @@ -1,34 +1,34 @@ +.. include:: + .. _audioai-compile-evaluate: -########################### +########################## Model Compile and Evaluate -########################### - -.. |tm| unicode:: U+2122 +########################## The :ref:`Audio AI Model Zoo ` runs *pre-compiled* artifacts on the |__PART_FAMILY_NAME__| target. This section covers the **x86 host workflow** that produces those artifacts: how to compile an audio model for -the C7\ |tm| NPU, run host-emulation inference, and measure accuracy against a -dataset — using **edgeai-tidlrunner**. It runs entirely on an Ubuntu x86 PC; +the C7\ |trade| NPU, run host-emulation inference, and measure accuracy against a +dataset - using **edgeai-tidlrunner**. It runs entirely on an Ubuntu x86 PC; nothing here touches the target. This is the workflow for *any* audio model, not just the reference models. The four reference models below are worked examples. To bring your own model you point the same ``tidlrunner-cli`` at your ONNX graph and a per-model YAML config, using a reference config as a starting template. Which operators offload to the -C7\ |tm| NPU (vs. fall back to the Arm core) is determined by TIDL — see +C7\ |trade| NPU (vs. fall back to the Arm core) is determined by TIDL - see `edgeai-tidl-tools `__ for the authoritative list of supported operators. -******************** +***************** edgeai-tidlrunner -******************** +***************** edgeai-tidlrunner is a bring-your-own-model (BYOM) command-line wrapper over the core `edgeai-tidl-tools `__ compilation stack. A single YAML config per model drives every stage, and one -CLI — ``tidlrunner-cli`` — exposes the stages this section uses: +CLI - ``tidlrunner-cli`` - exposes the stages this section uses: .. list-table:: :header-rows: 1 @@ -55,78 +55,78 @@ reused across ``compile``, ``infer``, and ``evaluate``. .. _audioai-am62d-setup: -******************** +***** Setup -******************** +***** Model compilation runs on an x86 PC (Ubuntu Linux recommended). The AM62D audio flow is self-contained: a single setup script installs the whole TVM toolchain into a dedicated Python 3.10 environment. The TIDL tools target Python 3.10. These steps assume `pyenv `__. -**1. Clone the repository** (|__TIDLRUNNER_REPO_URL__|): +#. **Clone the repository** (https://github.com/TexasInstruments/edgeai-tidlrunner): -.. code-block:: console + .. code-block:: console - $ git clone https://github.com/TexasInstruments/edgeai-tidlrunner.git - $ cd edgeai-tidlrunner + $ git clone https://github.com/TexasInstruments/edgeai-tidlrunner.git + $ cd edgeai-tidlrunner -**2. Create and activate a dedicated Python 3.10 environment.** The AM62D flow -uses a TI TVM wheel; keep it in its own venv (``tidlrunner-am62d``) -so it never clobbers a standard ``tidlrunner`` setup: +#. **Create and activate a dedicated Python 3.10 environment.** The AM62D flow + uses a TI TVM wheel; keep it in its own venv (``tidlrunner-am62d``) + so it never clobbers a standard ``tidlrunner`` setup: -.. code-block:: console + .. code-block:: console - $ pyenv install 3.10 - $ pyenv virtualenv 3.10 tidlrunner-am62d - $ pyenv activate tidlrunner-am62d + $ pyenv install 3.10 + $ pyenv virtualenv 3.10 tidlrunner-am62d + $ pyenv activate tidlrunner-am62d -**3. Run the AM62D setup script.** With the venv active, one script installs -everything this flow needs: +#. **Run the AM62D setup script.** With the venv active, one script installs + everything this flow needs: -.. code-block:: console + .. code-block:: console - $ ./devices/setup_am62d.sh + $ ./devices/setup_am62d.sh -It downloads the ARM GCC 15.2 and C7000 CGT 5.0.0.LTS cross-toolchains into -``tools/tidl_tools_package/bin/`` (skipped if already present), installs the -x86 TI TVM wheel — which bundles the AM62D x86 TIDL tools *inside* -the package — and installs ``tidlrunner[pc,audio]``, ``tools``, ``onnxruntime``, -and ``tidl_onnx_model_optimizer``. The ``[audio]`` extra pulls in the audio -packages (``librosa``, ``soundfile``, ``scipy``, ``pesq``, ``pystoi``, -``scikit-learn``). No separate ``pip install`` is needed. + It downloads the ARM GCC 15.2 and C7000 CGT 5.0.0.LTS cross-toolchains into + ``tools/tidl_tools_package/bin/`` (skipped if already present), installs the + x86 TI TVM wheel - which bundles the AM62D x86 TIDL tools *inside* + the package - and installs ``tidlrunner[pc,audio]``, ``tools``, ``onnxruntime``, + and ``tidl_onnx_model_optimizer``. The ``[audio]`` extra pulls in the audio + packages (``librosa``, ``soundfile``, ``scipy``, ``pesq``, ``pystoi``, + ``scikit-learn``). No separate ``pip install`` is needed. -**4. Source the env script once per shell.** TVM ``dlopen``\ s the TIDL runtime -(``libvx_tidl_rt.so``) before Python's own import runs, so the tool paths must be -live in the shell *before* ``tidlrunner-cli`` starts. Source — do **not** execute -— ``devices/am62d_env.sh`` once; every ``tidlrunner-cli`` command run afterward in -that shell picks the vars up: +#. **Source the env script once per shell.** TVM ``dlopen``\ s the TIDL runtime + (``libvx_tidl_rt.so``) before Python's own import runs, so the tool paths must be + live in the shell *before* ``tidlrunner-cli`` starts. Source (do **not** execute) + ``devices/am62d_env.sh`` once; every ``tidlrunner-cli`` command run afterward in + that shell picks the vars up: -.. code-block:: console + .. code-block:: console - $ source devices/am62d_env.sh + $ source devices/am62d_env.sh -It exports: + It exports: -.. list-table:: - :header-rows: 1 - :widths: 26 74 - - * - Variable - - Value - * - ``TIDL_TOOLS_PATH`` - - ``/3rdparty/x86_tidl_tools/AM62D`` (resolved from the - active venv's installed ``tvm``) - * - ``LD_LIBRARY_PATH`` - - prepends ``TIDL_TOOLS_PATH`` so ``libvx_tidl_rt.so`` resolves - * - ``ARM64_GCC_PATH`` - - the aarch64 GNU toolchain installed by ``setup_am62d.sh`` under - ``tools/tidl_tools_package/bin/`` (override by exporting before sourcing) - * - ``CGT7X_ROOT`` - - the C7000 code-generation tools installed by ``setup_am62d.sh`` under - ``tools/tidl_tools_package/bin/`` (override by exporting before sourcing) - * - ``SOC`` - - ``am62d`` + .. list-table:: + :header-rows: 1 + :widths: 26 74 + + * - Variable + - Value + * - ``TIDL_TOOLS_PATH`` + - ``/3rdparty/x86_tidl_tools/AM62D`` (resolved from the + active venv's installed ``tvm``) + * - ``LD_LIBRARY_PATH`` + - prepends ``TIDL_TOOLS_PATH`` so ``libvx_tidl_rt.so`` resolves + * - ``ARM64_GCC_PATH`` + - the aarch64 GNU toolchain installed by ``setup_am62d.sh`` under + ``tools/tidl_tools_package/bin/`` (override by exporting before sourcing) + * - ``CGT7X_ROOT`` + - the C7000 code-generation tools installed by ``setup_am62d.sh`` under + ``tools/tidl_tools_package/bin/`` (override by exporting before sourcing) + * - ``SOC`` + - ``am62d`` Datasets ======== @@ -137,14 +137,14 @@ sets. Download them from the repository root: .. code-block:: console - $ # UrbanSound8K (~5.6 GB) — sound classification (VGGish11, YAMNet) + $ # UrbanSound8K (~5.6 GB) - sound classification (VGGish11, YAMNet) $ bash examples/audio/scripts/download_urbansound8k.sh - $ # VoiceBank-DEMAND-16k (~2 GB) — speech enhancement (GCRN, GTCRN) + $ # VoiceBank-DEMAND-16k (~2 GB) - speech enhancement (GCRN, GTCRN) $ python3 examples/audio/scripts/download_voicebank_demand.py They land under ``data/datasets/UrbanSound8K/`` and -``data/datasets/VoiceBank-DEMAND-16k/`` — the paths the model configs expect. +``data/datasets/VoiceBank-DEMAND-16k/`` - the paths the model configs expect. The classification configs use UrbanSound8K **fold 10** (837 samples) as the test set; the speech-enhancement configs use the VoiceBank-DEMAND-16k **test** set (824 files). @@ -162,8 +162,8 @@ Compile and evaluate Each model has one config that drives all stages. All four run under the ``tidlrunner-am62d`` environment with ``devices/am62d_env.sh`` already sourced -(see `Setup`_). Three models compile to the C7\ |tm| NPU through the TVM runtime; -GTCRN runs ARM-only through ONNX Runtime (no C7\ |tm| NPU offload) but uses the +(see `Setup`_). Three models compile to the C7\ |trade| NPU through the TVM runtime; +GTCRN runs ARM-only through ONNX Runtime (no C7\ |trade| NPU offload) but uses the same environment. The AM62D config paths are: .. list-table:: @@ -176,22 +176,22 @@ same environment. The AM62D config paths are: - Tensor Bits - Config path (relative to ``edgeai-tidlrunner/``) * - VGGish11 - - |__MODEL_ID_VGGISH11__| + - ``ac-0101`` - TVM-RT - 8 - ``data/configs/samples/models/audio/audio_classification/urbansound8k/vggish11_tvmrt_config.yaml`` * - YAMNet - - |__MODEL_ID_YAMNET__| + - ``ac-0201`` - TVM-RT - 8 - ``data/configs/samples/models/audio/audio_classification/urbansound8k/yamnet_tvmrt_config.yaml`` * - GCRN - - |__MODEL_ID_GCRN__| + - ``ase-0201`` - TVM-RT - 16 (LSTM), FP32 (Enc/Dec) - ``data/configs/samples/models/audio/speech_enhancement/voicebank_demand_16k/gcrn_fixed_4sec_tvmrt_config.yaml`` * - GTCRN - - |__MODEL_ID_GTCRN__| + - ``ase-0100`` - ONNX-RT - FP32 - ``data/configs/samples/models/audio/speech_enhancement/voicebank_demand_16k/gtcrn_dns3_config.yaml`` @@ -199,7 +199,7 @@ same environment. The AM62D config paths are: VGGish11 (TVM-RT, 8-bit) ======================== -Runs on the C7\ |tm| NPU (``tidl_offload: true``): +Runs on the C7\ |trade| NPU (``tidl_offload: true``): .. code-block:: console @@ -223,7 +223,7 @@ YAMNet (TVM-RT, 8-bit) GCRN (TVM-RT, 16-bit) ===================== -GCRN is C7\ |tm| NPU-offloaded at 16-bit (``tidl_offload: true``, fixed 4-second +GCRN is C7\ |trade| NPU-offloaded at 16-bit (``tidl_offload: true``, fixed 4-second input): .. code-block:: console @@ -239,7 +239,7 @@ GTCRN (ONNX-RT, ARM-only) ========================= GTCRN has a dynamic time axis and TIDL-unsupported operators, so it runs on the -Arm Cortex-A cores through ONNX Runtime (``tidl_offload: false``) — no C7\ |tm| NPU +Arm Cortex-A cores through ONNX Runtime (``tidl_offload: false``) - no C7\ |trade| NPU offload. It uses the same ``tidlrunner-am62d`` environment as the other models; it simply does not offload to the DSP: diff --git a/source/devices/AM62DX/edgeai/model_inspect.rst b/source/devices/AM62DX/edgeai/model_inspect.rst index 08d69f982..67b5fd91e 100644 --- a/source/devices/AM62DX/edgeai/model_inspect.rst +++ b/source/devices/AM62DX/edgeai/model_inspect.rst @@ -9,14 +9,14 @@ Model Inspector analyzes a model on the x86 host after (or instead of) the edgeai-tidlrunner installation and environment described in :ref:`Setup `. -******************** +*************** Model Inspector -******************** +*************** ``tidlrunner-cli inspect`` is a one-shot analysis pipeline: it compiles the model for TIDL, runs inference, re-runs a non-TIDL reference for comparison, computes per-layer signal-to-noise ratios, and emits a **self-contained -interactive HTML report** — no need to run ``compile`` and ``infer`` +interactive HTML report** - no need to run ``compile`` and ``infer`` separately. The report opens in any browser and lands at: .. code-block:: text @@ -29,12 +29,12 @@ runtime, task, dataset, and model, e.g. The report has three tabs: -- **Model Summary** — acceleration ratio (layers on the C7x DSP vs. Arm +- **Model Summary** - acceleration ratio (layers on the C7x DSP vs. Arm fallback), input/output specs, and an interactive ONNX graph. -- **Subgraphs** — the TIDL subgraph(s) offloaded to the DSP, with per-layer +- **Subgraphs** - the TIDL subgraph(s) offloaded to the DSP, with per-layer identity and a quantization analysis (INT8 vs. FP32 activation histograms and scatter plots). -- **Performance** — *estimated* per-layer processing time, DSP cycle breakdown +- **Performance** - *estimated* per-layer processing time, DSP cycle breakdown (total / IO / kernel), memory usage (DDR / L2 / MSMC), and SNR metrics. .. note:: @@ -43,7 +43,7 @@ The report has three tabs: on-target measurements. Model Inspector requires TIDL tools **11.02 or later** (earlier versions emit SVG subgraph files it cannot read). -GCRN showcase — an offloaded DSP subgraph +GCRN showcase - an offloaded DSP subgraph ========================================= Because GCRN now compiles with ``tidl_offload: true`` on AM62D, its inspector @@ -58,7 +58,7 @@ Generate it with the AM62D env sourced (see :ref:`Setup `): $ tidlrunner-cli inspect --config_path $CFG For contrast, running ``inspect`` on the GTCRN (ONNX-RT) config produces a -report with **no** DSP subgraph — every layer is Arm fallback — which is what a +report with **no** DSP subgraph - every layer is Arm fallback - which is what a non-offloaded model looks like in the inspector. GTCRN also runs with ``tidl_offload: false``, so its work directory uses a ``notidl`` segment in place of the ```` shown in the path template above, e.g. diff --git a/source/devices/AM62DX/edgeai/model_zoo.rst b/source/devices/AM62DX/edgeai/model_zoo.rst index e452a694f..a95d7a725 100644 --- a/source/devices/AM62DX/edgeai/model_zoo.rst +++ b/source/devices/AM62DX/edgeai/model_zoo.rst @@ -1,11 +1,11 @@ +.. include:: + .. _audioai-model-zoo: ################## Audio AI Model Zoo ################## -.. |tm| unicode:: U+2122 - The AudioAI ModelZoo delivers out-of-box audio deep-learning demos that run directly on the |__PART_FAMILY_NAME__| target. Each reference model ships as an `ONNX `__ graph plus, where applicable, a pre-compiled @@ -21,9 +21,9 @@ in :ref:`Model Compile and Evaluate `. The models in this repository are made available for experimentation and development. They are not intended for deployment in production. -******************** +********** What ships -******************** +********** .. list-table:: :header-rows: 1 @@ -35,75 +35,75 @@ What ships - On target - Demo * - VGGish11 - - Sound classification (|__DATASET_AUDIO_CLS__|) - - |__MODEL_ID_VGGISH11__| - - C7\ |tm| NPU (TVM-RT + TIDL, 8-bit) + - Sound classification (UrbanSound8K) + - ``ac-0101`` + - C7\ |trade| NPU (TVM-RT + TIDL, 8-bit) - Script + notebook * - YAMNet - - Sound classification (|__DATASET_AUDIO_CLS__|) - - |__MODEL_ID_YAMNET__| - - C7\ |tm| NPU (TVM-RT + TIDL, 8-bit) + - Sound classification (UrbanSound8K) + - ``ac-0201`` + - C7\ |trade| NPU (TVM-RT + TIDL, 8-bit) - Script + notebook * - GCRN - - Speech enhancement (|__DATASET_SPEECH_ENH__|) - - |__MODEL_ID_GCRN__| - - C7\ |tm| NPU (TVM-RT + TIDL, 16-bit) + - Speech enhancement (VoiceBank-DEMAND-16k) + - ``ase-0201`` + - C7\ |trade| NPU (TVM-RT + TIDL, 16-bit) - Script + notebook * - GTCRN - - Speech enhancement (|__DATASET_SPEECH_ENH__|) - - |__MODEL_ID_GTCRN__| + - Speech enhancement (VoiceBank-DEMAND-16k) + - ``ase-0100`` - Arm core (ONNX runtime, FP32) - Script + notebook VGGish11, YAMNet, and GCRN each ship a pre-compiled TVM-RT + TIDL artifact and -run offloaded to the C7\ |tm| NPU. GTCRN ships **no** compiled artifact — it +run offloaded to the C7\ |trade| NPU. GTCRN ships **no** compiled artifact - it runs FP32 on the Arm core through the ONNX runtime, packaged as a plain ONNX Runtime artifact. -******************** +*************** On-target setup -******************** +*************** The steps below run on the AM62D target (aarch64), on the Linux command line of the Processor SDK rootfs. The notebooks and scripts run in a Python virtual environment that reuses the TIDL-enabled TVM runtime already present in the rootfs. -**1. Clone the repository.** Clone the AudioAI ModelZoo repository -(|__MODELZOO_REPO_URL__|): +#. **Clone the repository.** Clone the AudioAI ModelZoo repository + (https://github.com/TexasInstruments/audioai-modelzoo): -.. code-block:: console + .. code-block:: console - $ mkdir -p ~/tidl && cd ~/tidl - $ git clone https://github.com/TexasInstruments/audioai-modelzoo.git - $ cd audioai-modelzoo + $ mkdir -p ~/tidl && cd ~/tidl + $ git clone https://github.com/TexasInstruments/audioai-modelzoo.git + $ cd audioai-modelzoo -**2. Download the model artifacts.** Downloads the three pre-compiled -TVM-RT + TIDL artifacts (VGGish11, YAMNet, GCRN) plus the GTCRN ONNX Runtime -artifact into ``model_artifacts//am62d/``: +#. **Download the model artifacts.** Downloads the three pre-compiled + TVM-RT + TIDL artifacts (VGGish11, YAMNet, GCRN) plus the GTCRN ONNX Runtime + artifact into ``model_artifacts//am62d/``: -.. code-block:: console + .. code-block:: console - $ ./download_models.sh -y + $ ./download_models.sh -y -The script defaults to an interactive selection menu; the ``-y`` flag -downloads everything non-interactively. Add ``-l`` to print the source URLs -without downloading. + The script defaults to an interactive selection menu; the ``-y`` flag + downloads everything non-interactively. Add ``-l`` to print the source URLs + without downloading. -**3. Create the virtual environment.** Creates the venv at -|__TARGET_VENV_PATH__| with ``--system-site-packages`` so the TIDL-enabled -runtime from the rootfs is visible: +#. **Create the virtual environment.** Creates the venv at + ~/venv/modelzoo with ``--system-site-packages`` so the TIDL-enabled + runtime from the rootfs is visible: -.. code-block:: console + .. code-block:: console - $ ./setup_venv.sh + $ ./setup_venv.sh -**4. Activate the venv.** Activate it in every new shell before running the -demos: +#. **Activate the venv.** Activate it in every new shell before running the + demos: -.. code-block:: console + .. code-block:: console - $ source ~/venv/modelzoo/bin/activate + $ source ~/venv/modelzoo/bin/activate ********************** Inference script demos @@ -120,7 +120,7 @@ YAMNet (sound classification) $ cd ~/tidl/audioai-modelzoo/inference/yamnet_sc $ python3 yamnet_infer_audio.py --audio-file samples/miaow_16k.wav -The script preprocesses the audio into log-mel patches, runs each patch through +The script converts the audio into log-mel patches, runs each patch through the TVM-RT + TIDL session, and averages the per-patch scores. For the bundled ``miaow_16k.wav`` clip the top classes are cat/animal sounds. Expected output (abridged): @@ -161,7 +161,7 @@ VGGish11 (sound classification) $ cd ~/tidl/audioai-modelzoo/inference/vggish11_sc $ python3 vggish_infer_audio.py --audio-file sample_wav/139951-9-0-9.wav -The bundled clip is |__DATASET_AUDIO_CLS__| class 9 (street music). Expected +The bundled clip is UrbanSound8K class 9 (street music). Expected output (abridged): .. code-block:: text @@ -215,7 +215,7 @@ GTCRN (speech enhancement) $ cd ~/tidl/audioai-modelzoo/inference/gtcrn_se $ python3 gtcrn_infer_audio.py --input sample_wav/mix.wav -Unlike the other three models, GTCRN has no TVM-RT + TIDL artifact — the script +Unlike the other three models, GTCRN has no TVM-RT + TIDL artifact - the script loads the ONNX graph directly and runs it on ``CPUExecutionProvider`` (Arm core, FP32). It writes the enhanced audio to ``enh_onnx.wav`` beside the input; pass ``--output`` to change the destination wav. Expected output (abridged): @@ -281,9 +281,9 @@ its notebook from the file browser in the left sidebar. The notebooks are: * - YAMNet - ``inference/yamnet_sc/yamnet_inference.ipynb`` -******************** +****************** Compiled artifacts -******************** +****************** ``download_models.sh`` places the artifacts under ``model_artifacts//am62d/``. Each model gets its own directory, @@ -292,22 +292,22 @@ model) and, where applicable, an ``artifacts/`` folder (the compiled model artifacts). VGGish11, YAMNet, and GCRN each get a pre-compiled TVM-RT + TIDL artifact: -- |__MODEL_ID_VGGISH11__| — ``ac-0101_tvmrt_audio_classification_urbansound8k_vggish11_onnx`` (VGGish11) -- |__MODEL_ID_YAMNET__| — ``ac-0201_tvmrt_audio_classification_urbansound8k_yamnet_onnx`` (YAMNet) -- |__MODEL_ID_GCRN__| — ``ase-0201_tvmrt_speech_enhancement_voicebank_demand_16k_gcrn_fixed_4sec_onnx`` (GCRN) +- ``ac-0101`` - ``ac-0101_tvmrt_audio_classification_urbansound8k_vggish11_onnx`` (VGGish11) +- ``ac-0201`` - ``ac-0201_tvmrt_audio_classification_urbansound8k_yamnet_onnx`` (YAMNet) +- ``ase-0201`` - ``ase-0201_tvmrt_speech_enhancement_voicebank_demand_16k_gcrn_fixed_4sec_onnx`` (GCRN) Each of those artifact directories' ``artifacts/`` folder holds the TVM-RT -deployables (``deploy_lib.so``, ``deploy_graph.json``, ``deploy_param.params``) +deployment artifacts (``deploy_lib.so``, ``deploy_graph.json``, ``deploy_param.params``) in both target (``.evm``, aarch64) and host (``.pc``, x86) variants. The demo scripts and notebooks select the ``.evm`` variant automatically on the target. -GTCRN (|__MODEL_ID_GTCRN__|) gets an ONNX Runtime artifact instead of a +GTCRN (``ase-0100``) gets an ONNX Runtime artifact instead of a compiled one: -- |__MODEL_ID_GTCRN__| — ``ase-0100_onnxrt_speech_enhancement_voicebank_demand_16k_gtcrn_dns3_onnx`` (GTCRN) +- ``ase-0100`` - ``ase-0100_onnxrt_speech_enhancement_voicebank_demand_16k_gtcrn_dns3_onnx`` (GTCRN) Its directory holds only the ``model/`` folder, with the ``gtcrn_dns3.onnx`` -graph — there is no ``artifacts/`` folder since no TIDL compilation is +graph - there is no ``artifacts/`` folder since no TIDL compilation is involved. The demo script and notebook load the ONNX model directly with ``CPUExecutionProvider`` and run FP32 on the Arm core. Compiled TVM-RT + TIDL artifacts for your own models are produced by the x86 workflow in diff --git a/source/devices/AM62DX/edgeai/performance.rst b/source/devices/AM62DX/edgeai/performance.rst index 39a518d1c..2a1471f27 100644 --- a/source/devices/AM62DX/edgeai/performance.rst +++ b/source/devices/AM62DX/edgeai/performance.rst @@ -1,12 +1,12 @@ +.. include:: + .. _audioai-performance: ##################### Performance Benchmark ##################### -.. |tm| unicode:: U+2122 - -This section reports **inference performance** — latency and memory — for the +This section reports **inference performance** - latency and memory - for the reference |__PART_FAMILY_NAME__| Audio AI models. The same methodology (perfsim estimate + on-target measured) applies to any model you compile through the workflow. It is **not** an accuracy or audio-quality benchmark: model quality @@ -30,17 +30,17 @@ confused: - Produced by the TIDL performance simulator during ``compile``. The compiler writes a ``result.yaml`` per model under its work directory; the ``perfsim`` latency, GMAC count, and estimated DDR transfer are - read directly from it. This is a *static estimate* for the C7\ |tm| - NPU subgraph — it does not include Arm-side pre/post-processing or + read directly from it. This is a *static estimate* for the C7\ |trade| + NPU subgraph - it does not include Arm-side pre/post-processing or runtime overhead. * - **on-target measured** - Wall-clock latency timed on the AM62D target while running the :ref:`Model Zoo ` inference scripts. For the streaming speech-enhancement models it is also expressed as a - **real-time factor (RTF)** = processing time ÷ audio duration; RTF < 1 + **real-time factor (RTF)** = processing time / audio duration; RTF < 1 means the model keeps up with real-time audio. -Only models compiled for the C7\ |tm| NPU (TVM-RT + TIDL) have a perfsim +Only models compiled for the C7\ |trade| NPU (TVM-RT + TIDL) have a perfsim estimate. GTCRN runs FP32 on the Arm core through the ONNX runtime and has no TIDL subgraph, so perfsim does not apply to it. @@ -65,25 +65,25 @@ Per-model results - GMACs - DDR transfer *(estimated)* * - VGGish11 - - |__MODEL_ID_VGGISH11__| + - ``ac-0101`` - 8-bit - 5.235 ms - 1.1515 - 10.73 MB * - YAMNet - - |__MODEL_ID_YAMNET__| + - ``ac-0201`` - 8-bit - 1.990 ms - 0.0692 - 3.55 MB * - GCRN - - |__MODEL_ID_GCRN__| + - ``ase-0201`` - 16-bit - TBD - TBD - TBD * - GTCRN - - |__MODEL_ID_GTCRN__| + - ``ase-0100`` - FP32 (Arm) - N/A - N/A @@ -94,7 +94,7 @@ above). GCRN is compiled at 16-bit but its ``result.yaml`` is not yet captured, so its perfsim figures are **TBD**. GTCRN is Arm-only (no TIDL subgraph), so perfsim is **N/A**. -On-target measured latency and RTF for all four models are **TBD** — to be +On-target measured latency and RTF for all four models are **TBD** - to be filled in from the Model Zoo inference scripts on the AM62D EVM: .. list-table:: @@ -106,15 +106,15 @@ filled in from the Model Zoo inference scripts on the AM62D EVM: - Measured latency - RTF * - VGGish11 - - C7\ |tm| NPU + - C7\ |trade| NPU - TBD - - — (clip classifier) + - - (clip classifier) * - YAMNet - - C7\ |tm| NPU + - C7\ |trade| NPU - TBD - - — (clip classifier) + - - (clip classifier) * - GCRN - - C7\ |tm| NPU + - C7\ |trade| NPU - TBD - TBD * - GTCRN

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ONNX audio model
(YAMNet, VGGish11, GCRN)
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Partitioning
TIDL import API
TVM C7x™ NPU
code generation
TIDL network compiler
TVM-RT deployable artifact
(deploy_lib.so + graph.json + params)
compile time (x86)
run time (AM62D)
TVM runtime
TIDL-RT
C7x™ NPU
unsupported ops
supported ops
Text is not SVG - cannot display \ No newline at end of file From 4c1c7d14be12cb43b28b7b56bd20ea5f91401975 Mon Sep 17 00:00:00 2001 From: JuneChul Roh Date: Wed, 2 Sep 2026 22:46:21 -0500 Subject: [PATCH 03/16] feat(am62d-audioai): add Section 2 Audio AI Model Zoo on-target demos Signed-off-by: JuneChul Roh --- source/devices/AM62DX/audioai/model_zoo.rst | 271 +++++++++++++++++++- 1 file changed, 270 insertions(+), 1 deletion(-) diff --git a/source/devices/AM62DX/audioai/model_zoo.rst b/source/devices/AM62DX/audioai/model_zoo.rst index f4aa9e608..337ab1918 100644 --- a/source/devices/AM62DX/audioai/model_zoo.rst +++ b/source/devices/AM62DX/audioai/model_zoo.rst @@ -4,4 +4,273 @@ Audio AI Model Zoo ################## -Placeholder for the AM62D Audio AI Model Zoo on-target demos. +.. |tm| unicode:: U+2122 + +The AudioAI ModelZoo delivers out-of-box audio deep-learning demos that run +directly on the |__PART_FAMILY_NAME__| target. Each supported model ships as an +`ONNX `__ graph plus, where applicable, a pre-compiled +TVM-RT + TIDL artifact, so you can reproduce inference on the AM62D without +compiling anything yourself. Two demo front-ends are provided: standalone +Python inference scripts and Jupyter notebooks. This section runs entirely +**on the target**; the x86 compile/evaluate flow is covered in +:ref:`Compile / Evaluate / Inspect `. + +.. note:: + + The models in this repository are made available for experimentation and + development. They are not intended for deployment in production. + +******************** +What ships +******************** + +.. list-table:: + :header-rows: 1 + :widths: 18 26 14 14 28 + + * - Model + - Task + - Model ID + - On target + - Demo + * - VGGish11 + - Sound classification (|__DATASET_AUDIO_CLS__|) + - |__MODEL_ID_VGGISH11__| + - C7x\ |tm| NPU (TVM-RT + TIDL, 8-bit) + - Script + notebook + * - YAMNet + - Sound classification (|__DATASET_AUDIO_CLS__|) + - |__MODEL_ID_YAMNET__| + - C7x\ |tm| NPU (TVM-RT + TIDL, 8-bit) + - Script + notebook + * - GCRN + - Speech enhancement (|__DATASET_SPEECH_ENH__|) + - |__MODEL_ID_GCRN__| + - C7x\ |tm| NPU (TVM-RT + TIDL, 16-bit) + - Script + notebook + * - GTCRN + - Speech enhancement (|__DATASET_SPEECH_ENH__|) + - |__MODEL_ID_GTCRN__| + - Arm core (ONNX runtime, FP32) + - Notebook only + +VGGish11, YAMNet, and GCRN each ship a pre-compiled artifact and run offloaded +to the C7x\ |tm| NPU through the TVM runtime. GTCRN ships **no** compiled +artifact — it runs FP32 on the Arm core through the ONNX runtime and is +demonstrated in a notebook only. + +******************** +On-target setup +******************** + +The steps below run on the AM62D target (aarch64), on the Linux command line of +the Processor SDK rootfs. The notebooks and scripts run in a Python virtual +environment that reuses the TIDL-enabled TVM/ONNX runtime already present in the +rootfs. + +**1. Clone the repository.** Clone the AudioAI ModelZoo repository +(|__MODELZOO_REPO_URL__|): + +.. code-block:: console + + $ mkdir -p ~/tidl && cd ~/tidl + $ git clone https://github.com/TexasInstruments-Sandbox/audioai-modelzoo.git + $ cd audioai-modelzoo + +**2. Download the ONNX models.** Downloads the four model graphs into +``models/onnx/``: + +.. code-block:: console + + $ ./download_models.sh -y + +**3. Download the compiled artifacts.** Downloads the three pre-compiled +TVM-RT + TIDL artifacts into ``model_artifacts/11_02_18_00/am62d/`` +(GTCRN has no artifact): + +.. code-block:: console + + $ ./download_artifacts.sh -y + +Both download scripts default to an interactive selection menu; the ``-y`` flag +downloads everything non-interactively. Add ``-l`` to print the source URLs +without downloading. + +**4. Create the virtual environment.** Creates the venv at +|__TARGET_VENV_PATH__| with ``--system-site-packages`` so the TIDL-enabled +runtime from the rootfs is visible, then verifies that +``TIDLExecutionProvider`` is available: + +.. code-block:: console + + $ ./venv/setup_venv.sh + +**5. Activate the venv.** Activate it in every new shell before running the +demos: + +.. code-block:: console + + $ source ~/venv/modelzoo/bin/activate + +********************** +Inference script demos +********************** + +Run each script from its own model directory in the activated venv. Three of +the four models ship a command-line script; GTCRN is demonstrated in a notebook +only (see :ref:`Jupyter notebook demos `). + +YAMNet (sound classification) +============================= + +.. code-block:: console + + $ cd ~/tidl/audioai-modelzoo/inference/yamnet_sc + $ python3 yamnet_infer_audio.py --audio-file samples/miaow_16k.wav + +The script preprocesses the audio into log-mel patches, runs each patch through +the TVM-RT + TIDL session, and averages the per-patch scores. For the bundled +``miaow_16k.wav`` clip the top classes are cat/animal sounds. Expected output +(abridged): + +.. code-block:: text + + ====================================================================== + YAMNET INFERENCE SUMMARY + ====================================================================== + + Audio File: miaow_16k.wav + Artifacts: ac-0201_tvmrt_audio_classification_urbansound8k_yamnet_onnx + Audio Duration: 6.73 seconds + Number of Patches: 7 + + ====================================================================== + TOP 10 PREDICTIONS (averaged across all patches): + ====================================================================== + Rank Score Class + ---------------------------------------------------------------------- + 1 ... Cat + 2 ... Animal + ... + ====================================================================== + PERFORMANCE METRICS: + ====================================================================== + Inference Time (all patches): 17.53 ms total | 7 patches + RTF = 0.0026 (0.018s / 6.73s) + ====================================================================== + +A second sample, ``samples/speech_whistling2.wav``, is also included. + +VGGish11 (sound classification) +=============================== + +.. code-block:: console + + $ cd ~/tidl/audioai-modelzoo/inference/vggish11_sc + $ python3 vggish_infer_audio.py --audio-file sample_wav/139951-9-0-9.wav + +The bundled clip is |__DATASET_AUDIO_CLS__| class 9 (street music). Expected +output (abridged): + +.. code-block:: text + + ====================================================================== + VGGISH11 INFERENCE SUMMARY + ====================================================================== + + Audio File: 139951-9-0-9.wav + Artifacts: ac-0101_tvmrt_audio_classification_urbansound8k_vggish11_onnx + Audio Duration: 4.00 seconds + Top Class: Street music (Class 9) - Score: ... + + Performance: + Inference: 8.88 ms + Throughput: RTF = 0.0022 (0.009s / 4.00s) + ====================================================================== + +GCRN (speech enhancement) +========================= + +.. code-block:: console + + $ cd ~/tidl/audioai-modelzoo/inference/gcrn_se + $ python3 gcrn_infer_audio.py --input sample_wav/noisy.wav + +The script writes the enhanced audio to ``enhanced.wav`` beside the input and a +per-block timing report to ``gcrn-benchmark.md``. Pass ``--output`` to change +the destination wav. Expected output (abridged): + +.. code-block:: text + + Machine: aarch64 SOC: am62d TIDL_VER: 11_02_18_00 + Loading TVM + TIDL session... + Running inference on noisy.wav (9.77s)... + Wrote enhanced audio to sample_wav/enhanced.wav + + GCRN TVM+TIDL benchmark (3 chunk(s), 9.77s audio) + + block total (ms) per-chunk (ms) + ... + Real-Time Factor (inference / audio) = 3.9500 + + Wrote benchmark report to sample_wav/gcrn-benchmark.md + +.. _audioai-model-zoo-notebooks: + +********************** +Jupyter notebook demos +********************** + +All four models — including GTCRN — have a Jupyter notebook demo. Start Jupyter +Lab from the activated venv: + +.. code-block:: console + + $ cd ~/tidl/audioai-modelzoo + $ ./venv/jupyter_lab_venv.sh + +The launcher prints a highlighted access URL and pre-loads the four inference +notebooks in tabs. Open the URL in a browser on a machine that can reach the +target and enter the token ``tidl`` when prompted: + +.. code-block:: text + + Access URL: http://:8888/lab?token=tidl + +The notebooks are: + +.. list-table:: + :header-rows: 1 + :widths: 22 40 + + * - Model + - Notebook + * - GCRN + - ``inference/gcrn_se/gcrn_inference.ipynb`` + * - GTCRN + - ``inference/gtcrn_se/gtcrn_inference.ipynb`` + * - VGGish11 + - ``inference/vggish11_sc/vggish_inference.ipynb`` + * - YAMNet + - ``inference/yamnet_sc/yamnet_inference.ipynb`` + +******************** +Compiled artifacts +******************** + +``download_artifacts.sh`` places the pre-compiled TVM-RT + TIDL artifacts under +``model_artifacts/11_02_18_00/am62d/``: + +- |__MODEL_ID_VGGISH11__| — ``ac-0101_tvmrt_audio_classification_urbansound8k_vggish11_onnx`` (VGGish11) +- |__MODEL_ID_YAMNET__| — ``ac-0201_tvmrt_audio_classification_urbansound8k_yamnet_onnx`` (YAMNet) +- |__MODEL_ID_GCRN__| — ``ase-0201_tvmrt_speech_enhancement_voicebank_demand_16k_gcrn_fixed_4sec_onnx`` (GCRN) + +Each artifact directory holds the TVM-RT deployables (``deploy_lib.so``, +``deploy_graph.json``, ``deploy_param.params``) in both target (``.evm``, +aarch64) and host (``.pc``, x86) variants. The demo scripts and notebooks +select the ``.evm`` variant automatically on the target. + +GTCRN (|__MODEL_ID_GTCRN__|) ships **no** compiled artifact: it runs FP32 on the +Arm core through the ONNX runtime directly from its ONNX graph. These artifacts +are produced by the x86 workflow in +:ref:`Compile / Evaluate / Inspect `. From 6f1644787c22474474ebab6030716d333eab8830 Mon Sep 17 00:00:00 2001 From: JuneChul Roh Date: Wed, 2 Sep 2026 22:53:24 -0500 Subject: [PATCH 04/16] feat(am62d-audioai): add Section 3 compile/evaluate/inspect workflow Signed-off-by: JuneChul Roh --- .../audioai/compile_evaluate_inspect.rst | 315 +++++++++++++++++- 1 file changed, 314 insertions(+), 1 deletion(-) diff --git a/source/devices/AM62DX/audioai/compile_evaluate_inspect.rst b/source/devices/AM62DX/audioai/compile_evaluate_inspect.rst index 9d575aa1b..5e1fb57cc 100644 --- a/source/devices/AM62DX/audioai/compile_evaluate_inspect.rst +++ b/source/devices/AM62DX/audioai/compile_evaluate_inspect.rst @@ -4,4 +4,317 @@ Compile / Evaluate / Inspect ############################ -Placeholder for the x86 compile, evaluate, and inspect workflow. +.. |tm| unicode:: U+2122 + +The :ref:`Audio AI Model Zoo ` runs *pre-compiled* artifacts +on the |__PART_FAMILY_NAME__| target. This section covers the **x86 host +workflow** that produces those artifacts: how to compile each audio model for +the C7x\ |tm| NPU, run host-emulation inference, and measure accuracy against a +dataset — using **edgeai-tidlrunner**. It runs entirely on an Ubuntu x86 PC; +nothing here touches the target. + +******************** +edgeai-tidlrunner +******************** + +edgeai-tidlrunner is a bring-your-own-model (BYOM) command-line wrapper over the +core `edgeai-tidl-tools `__ +compilation stack. A single YAML config per model drives every stage, and one +CLI — ``tidlrunner-cli`` — exposes the stages this section uses: + +.. list-table:: + :header-rows: 1 + :widths: 18 82 + + * - Command + - What it does + * - ``compile`` + - Imports the ONNX graph, quantizes and partitions it, and writes the + deployable TIDL/TVM-RT artifacts. + * - ``infer`` + - Runs the compiled model on the x86 host (PC emulation) to check output + correctness before deploying to the target. + * - ``evaluate`` + - Runs the compiled model over a dataset and reports accuracy or + speech-enhancement metrics. + * - ``inspect`` + - One-shot compile + inference + reference comparison that emits an + interactive HTML analysis report (see `Model Inspector`_). + +All commands are run from the repository root (``edgeai-tidlrunner/``) and take +``--config_path `` pointing at the per-model YAML. The same config file is +reused across ``compile``, ``infer``, and ``evaluate``. + +******************** +Setup +******************** + +Model compilation runs on an x86 PC (Ubuntu Linux recommended). The TIDL tools +used for compilation target Python 3.10, so create a dedicated Python 3.10 +environment. These steps assume `pyenv `__. + +**1. Clone the repository** (|__TIDLRUNNER_REPO_URL__|): + +.. code-block:: console + + $ git clone https://bitbucket.itg.ti.com/scm/edgeai-algo/edgeai-tidlrunner.git + $ cd edgeai-tidlrunner + +**2. Create and activate the Python 3.10 environment.** + +.. code-block:: console + + $ pyenv install 3.10 + $ pyenv virtualenv 3.10 tidlrunner + $ pyenv activate tidlrunner + +**3. Run the PC setup script.** This downloads the TIDL tools into +``tools/tidl_tools_package/`` and installs the runner: + +.. code-block:: console + + $ ./setup_runner_pc.sh + +The TIDL tools version is pinned by ``TIDL_TOOLS_VERSION`` inside +``setup_runner_pc.sh`` and can be overridden on the command line. **The tools +version used to compile must match the version on the target** — artifacts +compiled for a different TIDL version will not run: + +.. code-block:: console + + $ TIDL_TOOLS_VERSION="11.2.x" ./setup_runner_pc.sh + +**4. Install the audio extras.** The audio pipelines need extra Python packages +(``librosa``, ``soundfile``, ``scipy``, ``pesq``, ``pystoi``, ``scikit-learn``): + +.. code-block:: console + + $ pip install -e "tidlrunner[audio]" + +Datasets +======== + +Accuracy evaluation needs the reference datasets. Compilation alone can run on +random data (for latency-only measurement), but ``evaluate`` requires the real +sets. Download them from the repository root: + +.. code-block:: console + + $ # UrbanSound8K (~5.6 GB) — sound classification (VGGish11, YAMNet) + $ bash examples/audio/scripts/download_urbansound8k.sh + + $ # VoiceBank-DEMAND-16k (~2 GB) — speech enhancement (GCRN, GTCRN) + $ python3 examples/audio/scripts/download_voicebank_demand.py + +They land under ``data/datasets/UrbanSound8K/`` and +``data/datasets/VoiceBank-DEMAND-16k/`` — the paths the model configs expect. +The classification configs use UrbanSound8K **fold 10** (837 samples) as the +test set; the speech-enhancement configs use the VoiceBank-DEMAND-16k test set +(824 files). + +.. _audioai-am62d-interim-setup: + +Interim AM62D TVM-RT compile path +================================= + +.. warning:: + + The AM62D C7x\ |tm| compile path for the three TVM-RT models (VGGish11, + YAMNet, GCRN) is currently an **interim feasibility flow**, not the + productized ``setup_runner_pc.sh`` / ``TIDL_TOOLS_VERSION`` download. It is + documented here for reproducibility and will be replaced before release. + +The three TVM-RT models compile through a **dedicated environment** +(``tidlrunner-am62d``) that has the release-candidate TVM wheel installed. That +wheel ships the x86 AM62D TIDL tools *inside* the package, so the AM62D compile +uses the wheel-bundled tools rather than the ones ``setup_runner_pc.sh`` +downloads. TVM ``dlopen``\ s the TIDL runtime before Python's own import runs, +so the tool paths must be exported into the environment *before* ``tidlrunner-cli`` +starts. A small wrapper, ``agent_ws/am62d-run.sh``, does this and then runs the +command you pass it. It exports: + +.. list-table:: + :header-rows: 1 + :widths: 26 74 + + * - Variable + - Value + * - ``TIDL_TOOLS_PATH`` + - ``/3rdparty/x86_tidl_tools/AM62D`` (resolved from the + active venv's installed ``tvm``) + * - ``LD_LIBRARY_PATH`` + - prepends ``TIDL_TOOLS_PATH`` so ``libvx_tidl_rt.so`` resolves + * - ``ARM64_GCC_PATH`` + - path to your aarch64 GNU toolchain (e.g. ````) + * - ``CGT7X_ROOT`` + - path to your C7000 code-generation tools (e.g. ````) + * - ``SOC`` + - ``am62d`` + +Activate the dedicated environment, then prefix each TVM-RT command with the +wrapper (the whole ``tidlrunner-cli`` invocation is passed as one quoted +argument): + +.. code-block:: console + + $ pyenv activate tidlrunner-am62d + $ bash agent_ws/am62d-run.sh "tidlrunner-cli compile --config_path " + +.. note:: + + GTCRN does **not** use this wrapper. It runs ARM-only through ONNX Runtime + (``tidl_offload: false``) and compiles under the standard ``tidlrunner`` + environment from steps 1–4. + +.. TODO(WI-004): replace the interim ``agent_ws/am62d-run.sh`` wrapper + + ``tidlrunner-am62d`` venv with the productized ``setup_runner_pc.sh`` / + ``TIDL_TOOLS_VERSION`` AM62D flow before release. Confirm the final tools + version string (artifact folder 11_02_18_00 vs. RC bundled tools 11.02.16.00). + +******************** +Compile and evaluate +******************** + +Each model has one config that drives all stages. Three models compile to the +C7x\ |tm| NPU through the TVM runtime; GTCRN is the ONNX-RT exception that runs +ARM-only. The final AM62D config paths are: + +.. list-table:: + :header-rows: 1 + :widths: 14 10 12 10 54 + + * - Model + - Model ID + - Runtime + - Bits + - Config path (relative to ``edgeai-tidlrunner/``) + * - VGGish11 + - |__MODEL_ID_VGGISH11__| + - TVM-RT + - 8 + - ``data/configs/samples/models/audio/audio_classification/urbansound8k/vggish11_tvmrt_config.yaml`` + * - YAMNet + - |__MODEL_ID_YAMNET__| + - TVM-RT + - 8 + - ``data/configs/samples/models/audio/audio_classification/urbansound8k/yamnet_tvmrt_config.yaml`` + * - GCRN + - |__MODEL_ID_GCRN__| + - TVM-RT + - 16 + - ``data/configs/samples/models/audio/speech_enhancement/voicebank_demand_16k/gcrn_fixed_4sec_tvmrt_config.yaml`` + * - GTCRN + - |__MODEL_ID_GTCRN__| + - ONNX-RT + - FP32 + - ``data/configs/samples/models/audio/speech_enhancement/voicebank_demand_16k/gtcrn_dns3_config.yaml`` + +VGGish11 (TVM-RT, 8-bit) +======================== + +Runs on the C7x\ |tm| NPU (``tidl_offload: true``). Use the AM62D wrapper: + +.. code-block:: console + + $ CFG=data/configs/samples/models/audio/audio_classification/urbansound8k/vggish11_tvmrt_config.yaml + $ bash agent_ws/am62d-run.sh "tidlrunner-cli compile --config_path $CFG" + $ bash agent_ws/am62d-run.sh "tidlrunner-cli infer --config_path $CFG" + $ bash agent_ws/am62d-run.sh "tidlrunner-cli evaluate --config_path $CFG" + +``evaluate`` reports top-1 / top-5 / macro-F1 over UrbanSound8K fold 10. + +YAMNet (TVM-RT, 8-bit) +====================== + +.. code-block:: console + + $ CFG=data/configs/samples/models/audio/audio_classification/urbansound8k/yamnet_tvmrt_config.yaml + $ bash agent_ws/am62d-run.sh "tidlrunner-cli compile --config_path $CFG" + $ bash agent_ws/am62d-run.sh "tidlrunner-cli infer --config_path $CFG" + $ bash agent_ws/am62d-run.sh "tidlrunner-cli evaluate --config_path $CFG" + +GCRN (TVM-RT, 16-bit) +===================== + +GCRN is C7x\ |tm|-offloaded at 16-bit (``tidl_offload: true``, fixed 4-second +input). Same wrapper flow: + +.. code-block:: console + + $ CFG=data/configs/samples/models/audio/speech_enhancement/voicebank_demand_16k/gcrn_fixed_4sec_tvmrt_config.yaml + $ bash agent_ws/am62d-run.sh "tidlrunner-cli compile --config_path $CFG" + $ bash agent_ws/am62d-run.sh "tidlrunner-cli infer --config_path $CFG" + $ bash agent_ws/am62d-run.sh "tidlrunner-cli evaluate --config_path $CFG" + +``evaluate`` reports PESQ / STOI / SI-SDR over the VoiceBank-DEMAND-16k test set. + +GTCRN (ONNX-RT, ARM-only) +========================= + +GTCRN has a dynamic time axis and TIDL-unsupported operators, so it runs on the +Arm Cortex-A cores through ONNX Runtime (``tidl_offload: false``) — no C7x\ |tm| +offload and **no** AM62D wrapper. Run it directly under the standard +``tidlrunner`` environment: + +.. code-block:: console + + $ pyenv activate tidlrunner + $ CFG=data/configs/samples/models/audio/speech_enhancement/voicebank_demand_16k/gtcrn_dns3_config.yaml + $ tidlrunner-cli compile --config_path $CFG --target_device AM62D + $ tidlrunner-cli infer --config_path $CFG --target_device AM62D + $ tidlrunner-cli evaluate --config_path $CFG --target_device AM62D + +.. note:: + + The shipped ``gtcrn_dns3_config.yaml`` sets ``target_device: AM62A``; because + the model is ARM-only, execution is device-independent. Passing + ``--target_device AM62D`` keeps its work directory under the AM62D tree, + consistent with the other three models. + +******************** +Model Inspector +******************** + +``tidlrunner-cli inspect`` is a one-shot analysis pipeline: it compiles the +model for TIDL, runs inference, re-runs a non-TIDL reference for comparison, +computes per-layer signal-to-noise ratios, and emits a **self-contained +interactive HTML report** — no need to run ``compile`` and ``infer`` +separately. The report opens in any browser and lands at: + +.. code-block:: text + + work_dirs/compile////inspector/modelinspector.html + +The report has three tabs: + +- **Model Summary** — acceleration ratio (layers on the C7x\ |tm| DSP vs. Arm + fallback), input/output specs, and an interactive ONNX graph. +- **Subgraphs** — the TIDL subgraph(s) offloaded to the DSP, with per-layer + identity and a quantization analysis (INT8 vs. FP32 activation histograms and + scatter plots). +- **Performance** — *estimated* per-layer processing time, DSP cycle breakdown + (total / IO / kernel), memory usage (DDR / L2 / MSMC), and SNR metrics. + +.. note:: + + All Performance-tab numbers are **estimates** from the compiler, not + on-target measurements. Model Inspector requires TIDL tools **11.02 or + later** (earlier versions emit SVG subgraph files it cannot read). + +GCRN showcase — an offloaded DSP subgraph +========================================= + +Because GCRN now compiles with ``tidl_offload: true`` on AM62D, its inspector +report shows a **real C7x\ |tm| DSP subgraph** rather than an all-Arm fallback: +the Subgraphs tab lists a single offloaded subgraph (16-bit) with its layer +mapping, and the Performance tab breaks that subgraph down by cycles and memory. +Generate it through the AM62D wrapper: + +.. code-block:: console + + $ CFG=data/configs/samples/models/audio/speech_enhancement/voicebank_demand_16k/gcrn_fixed_4sec_tvmrt_config.yaml + $ bash agent_ws/am62d-run.sh "tidlrunner-cli inspect --config_path $CFG" + +For contrast, running ``inspect`` on the GTCRN (ONNX-RT) config produces a +report with **no** DSP subgraph — every layer is Arm fallback — which is what a +non-offloaded model looks like in the inspector. From 6cf0df019a93d0662f80c8151fe4956d0f2c4eef Mon Sep 17 00:00:00 2001 From: JuneChul Roh Date: Wed, 2 Sep 2026 22:56:20 -0500 Subject: [PATCH 05/16] feat(am62d-audioai): add Section 4 performance benchmark tables Signed-off-by: JuneChul Roh --- source/devices/AM62DX/audioai/performance.rst | 130 +++++++++++++++++- 1 file changed, 129 insertions(+), 1 deletion(-) diff --git a/source/devices/AM62DX/audioai/performance.rst b/source/devices/AM62DX/audioai/performance.rst index 829344546..0e28fdcd7 100644 --- a/source/devices/AM62DX/audioai/performance.rst +++ b/source/devices/AM62DX/audioai/performance.rst @@ -4,4 +4,132 @@ Performance Benchmark ##################### -Placeholder for the AM62D Audio AI performance benchmark tables. +.. |tm| unicode:: U+2122 + +This section reports **inference performance** — latency and memory — for the +four |__PART_FAMILY_NAME__| Audio AI models. It is **not** an accuracy or +audio-quality benchmark: model quality (classification accuracy, PESQ / STOI / +SI-SDR) is measured by the ``evaluate`` step in +:ref:`Compile / Evaluate / Inspect `. + +*********** +Methodology +*********** + +Two independent figures are reported for each model, and they must not be +confused: + +.. list-table:: + :header-rows: 1 + :widths: 22 78 + + * - Figure + - How it is obtained + * - **perfsim estimate** + - Produced by the TIDL performance simulator during ``compile``. The + compiler writes a ``result.yaml`` per model under its work directory; + the ``perfsim`` latency, GMAC count, and estimated DDR transfer are + read directly from it. This is a *static estimate* for the C7x\ |tm| + NPU subgraph — it does not include Arm-side pre/post-processing or + runtime overhead. + * - **on-target measured** + - Wall-clock latency timed on the AM62D target while running the + :ref:`Model Zoo ` inference scripts. For the + streaming speech-enhancement models it is also expressed as a + **real-time factor (RTF)** = processing time ÷ audio duration; RTF < 1 + means the model keeps up with real-time audio. + +Only models compiled for the C7x\ |tm| NPU (TVM-RT + TIDL) have a perfsim +estimate. GTCRN runs FP32 on the Arm core through the ONNX runtime and has no +TIDL subgraph, so perfsim does not apply to it. + +.. note:: + + The perfsim values below are read from ``result.yaml`` and are labelled + *estimated (perfsim)*. On-target measured latency and RTF are marked **TBD** + until measured on the AM62D EVM. + +***************** +Per-model results +***************** + +.. list-table:: + :header-rows: 1 + :widths: 16 12 12 20 14 26 + + * - Model + - Model ID + - Precision + - perfsim latency *(estimated)* + - GMACs + - DDR transfer *(estimated)* + * - VGGish11 + - |__MODEL_ID_VGGISH11__| + - 8-bit + - 5.235 ms + - 1.1515 + - 10.73 MB + * - YAMNet + - |__MODEL_ID_YAMNET__| + - 8-bit + - 1.990 ms + - 0.0692 + - 3.55 MB + * - GCRN + - |__MODEL_ID_GCRN__| + - 16-bit + - TBD + - TBD + - TBD + * - GTCRN + - |__MODEL_ID_GTCRN__| + - FP32 (Arm) + - N/A + - N/A + - N/A + +VGGish11 and YAMNet are compiled and have ``result.yaml`` perfsim values (shown +above). GCRN is compiled at 16-bit but its ``result.yaml`` is not yet captured, +so its perfsim figures are **TBD**. GTCRN is Arm-only (no TIDL subgraph), so +perfsim is **N/A**. + +On-target measured latency and RTF for all four models are **TBD** — to be +filled in from the Model Zoo inference scripts on the AM62D EVM: + +.. list-table:: + :header-rows: 1 + :widths: 20 20 30 30 + + * - Model + - On target + - Measured latency + - RTF + * - VGGish11 + - C7x\ |tm| NPU + - TBD + - — (clip classifier) + * - YAMNet + - C7x\ |tm| NPU + - TBD + - — (clip classifier) + * - GCRN + - C7x\ |tm| NPU + - TBD + - TBD + * - GTCRN + - Arm core + - TBD + - TBD + +RTF is reported only for the streaming speech-enhancement models (GCRN, GTCRN); +VGGish11 and YAMNet classify a whole clip, so a real-time factor is not +meaningful for them. + +******** +Non-goal +******** + +This section does **not** assess model quality. Classification accuracy and +speech-enhancement quality metrics are produced by ``tidlrunner-cli evaluate`` +and documented in +:ref:`Compile / Evaluate / Inspect `. From 0db7cfb3629579fa44c5e99d32d9e6d962392dbe Mon Sep 17 00:00:00 2001 From: JuneChul Roh Date: Wed, 2 Sep 2026 23:01:40 -0500 Subject: [PATCH 06/16] feat(am62d-audioai): integrate navigation, cross-links, and full build Signed-off-by: JuneChul Roh --- .../config/vocabularies/PSDK/accept.txt | 18 ++++++++++++++++++ configs/AM62DX/AM62DX_audioai_config.txt | 4 ++-- configs/AM62DX/AM62DX_audioai_tags.py | 4 ++-- .../audioai/compile_evaluate_inspect.rst | 8 ++++---- source/devices/AM62DX/audioai/performance.rst | 6 +++--- source/images/audioai/tvm_tidl_flow.drawio | 16 +++++++++++----- source/images/audioai/tvm_tidl_flow.png | Bin 119800 -> 127058 bytes source/images/audioai/tvm_tidl_flow.svg | 2 +- 8 files changed, 41 insertions(+), 17 deletions(-) diff --git a/.github/styles/config/vocabularies/PSDK/accept.txt b/.github/styles/config/vocabularies/PSDK/accept.txt index 9611e6dcb..ac18fcc3b 100644 --- a/.github/styles/config/vocabularies/PSDK/accept.txt +++ b/.github/styles/config/vocabularies/PSDK/accept.txt @@ -1,10 +1,17 @@ Armbian +BYOM BeagleBone Broadcom +C7x +DDR Debian +GCRN +GMACs GStreamer +GTCRN Jacinto Khronos +NPU NXP ONNX OpenCL @@ -12,15 +19,20 @@ OpenGL OpenVX PVRCarbon PVRTune +RTF Sitara Skia Slint TFLite +TIDL TVM Trixie UniFlash +VGGish11 +VoiceBank Vulkan Weston +YAMNet Yocto Zink [Bb]ackplane @@ -34,8 +46,14 @@ Zink [Rr]enderers? [Ss]carthgap [Tt]oolchain +aarch64 balenaEtcher bdebstrap +edgeai fdisk mmdebstrap +perfsim +pyenv +tidlrunner umount +venv diff --git a/configs/AM62DX/AM62DX_audioai_config.txt b/configs/AM62DX/AM62DX_audioai_config.txt index 49404d3c6..fe538eb3d 100644 --- a/configs/AM62DX/AM62DX_audioai_config.txt +++ b/configs/AM62DX/AM62DX_audioai_config.txt @@ -22,8 +22,8 @@ Replacement Variables '__AUDIOAI_SDK_VERSION__' : '12.1' '__TIDL_VERSION__' : '11_02_18_00' '__TIDL_TOOLS_VERSION__' : '11.2.x' -'__MODELZOO_REPO_URL__' : 'https://bitbucket.itg.ti.com/projects/AUDIOAI-ALGO/repos/audioai-modelzoo' -'__TIDLRUNNER_REPO_URL__' : 'https://bitbucket.itg.ti.com/projects/EDGEAI-ALGO/repos/edgeai-tidlrunner' +'__MODELZOO_REPO_URL__' : 'https://github.com/TexasInstruments-Sandbox/audioai-modelzoo' +'__TIDLRUNNER_REPO_URL__' : 'https://github.com/TexasInstruments/edgeai-tidlrunner' '__TIDL_TOOLS_REPO_URL__' : 'https://github.com/TexasInstruments/edgeai-tidl-tools' '__TARGET_VENV_PATH__' : '~/venv/modelzoo' '__DATASET_AUDIO_CLS__' : 'UrbanSound8K' diff --git a/configs/AM62DX/AM62DX_audioai_tags.py b/configs/AM62DX/AM62DX_audioai_tags.py index 66d9fecca..4e85bcd38 100644 --- a/configs/AM62DX/AM62DX_audioai_tags.py +++ b/configs/AM62DX/AM62DX_audioai_tags.py @@ -1,6 +1,6 @@ # Project name and HTML title -project = u'Audio AI SDK for AM62D' -html_title = 'Audio AI SDK for AM62D Documentation' +project = u'Audio AI for AM62D' +html_title = 'Audio AI for AM62D Documentation' # The master toctree document. master_doc = 'devices/AM62DX/audioai/index' diff --git a/source/devices/AM62DX/audioai/compile_evaluate_inspect.rst b/source/devices/AM62DX/audioai/compile_evaluate_inspect.rst index 5e1fb57cc..0cde3f574 100644 --- a/source/devices/AM62DX/audioai/compile_evaluate_inspect.rst +++ b/source/devices/AM62DX/audioai/compile_evaluate_inspect.rst @@ -1,8 +1,8 @@ .. _audioai-compile-evaluate-inspect: -############################ -Compile / Evaluate / Inspect -############################ +################################## +Model Compile / Evaluate / Inspect +################################## .. |tm| unicode:: U+2122 @@ -57,7 +57,7 @@ environment. These steps assume `pyenv `__. .. code-block:: console - $ git clone https://bitbucket.itg.ti.com/scm/edgeai-algo/edgeai-tidlrunner.git + $ git clone https://github.com/TexasInstruments/edgeai-tidlrunner.git $ cd edgeai-tidlrunner **2. Create and activate the Python 3.10 environment.** diff --git a/source/devices/AM62DX/audioai/performance.rst b/source/devices/AM62DX/audioai/performance.rst index 0e28fdcd7..67fd1355d 100644 --- a/source/devices/AM62DX/audioai/performance.rst +++ b/source/devices/AM62DX/audioai/performance.rst @@ -125,9 +125,9 @@ RTF is reported only for the streaming speech-enhancement models (GCRN, GTCRN); VGGish11 and YAMNet classify a whole clip, so a real-time factor is not meaningful for them. -******** -Non-goal -******** +******************* +Scope & Limitations +******************* This section does **not** assess model quality. Classification accuracy and speech-enhancement quality metrics are produced by ``tidlrunner-cli evaluate`` diff --git a/source/images/audioai/tvm_tidl_flow.drawio b/source/images/audioai/tvm_tidl_flow.drawio index 83b434ea0..2c5246eb2 100644 --- a/source/images/audioai/tvm_tidl_flow.drawio +++ b/source/images/audioai/tvm_tidl_flow.drawio @@ -19,7 +19,7 @@ - + @@ -51,8 +51,11 @@ + + + - + @@ -64,10 +67,10 @@ - + - + @@ -79,7 +82,10 @@ - + + + + diff --git a/source/images/audioai/tvm_tidl_flow.png b/source/images/audioai/tvm_tidl_flow.png index 6b5406f2f7682b7413a9265fe6c6e274d654066a..a5a7d3b8c05b6cd3c61d832cdefa8d0e2506c88d 100644 GIT binary patch delta 88687 zcmYhibyQW)_dk3U5d{I2P*K39L%P90q#LAFq`R9#hzJO%bT?cY=>}0i>AG|Y(%lWe zeczw;eb&QYF67)hb7p4mSIvF~Ue6@nmxss?XQkJ+W{zUkZ!Ha_E#B*yqEL>pE74ju zF+8Ln5kmH{0DF-)Qp_ja&isll?9yCSE*N={i)KE*F7ev$4mezBxb#8SVwtBr(4~7n z?CP$QZK^7s#_GedHlDU`ya89gdr+D&IvAh3I#`P9K}se0BmYC>G&+>y@5M7;iSEok zge*uduUO&w=WCut={MOVA3TjD>%GL3q#m3EPL5tJf7z2*mEwVX_xBUY$66 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ONNX audio model
(YAMNet, VGGish11, GCRN)
Model import into TVM
Partitioning
TIDL import API
TVM C7x™ NPU
code generation
TIDL network compiler
TVM-RT deployable artifact
(deploy_lib.so + graph.json + params)
compile time (x86)
run time (AM62D)
TVM runtime
TIDL-RT
C7x™ NPU
unsupported ops
supported ops
Text is not SVG - cannot display \ No newline at end of file +
ONNX audio model
(YAMNet, VGGish11, GCRN)
Model import into TVM
Partitioning
TIDL import API
TVM C7x™ DSP
code generation
TIDL network compiler
TVM-RT deployable artifact
(deploy_lib.so + graph.json + params)
compile time (x86)
run time (AM62D)
TVM runtime
TIDL-RT
Arm® core
C7x™ NPU
TIDL unsupported ops
TIDL supported ops
Text is not SVG - cannot display \ No newline at end of file From 283617cc38f1bdea4a07bf49373ec9c1ac0153d6 Mon Sep 17 00:00:00 2001 From: JuneChul Roh Date: Wed, 9 Sep 2026 16:41:13 -0500 Subject: [PATCH 07/16] docs(am62d-audioai): reframe as BYOM workflow and fix C7 NPU terminology Signed-off-by: JuneChul Roh --- .../audioai/compile_evaluate_inspect.rst | 87 ++++++++++-------- .../devices/AM62DX/audioai/introduction.rst | 79 ++++++++++------ source/devices/AM62DX/audioai/model_zoo.rst | 23 ++--- source/devices/AM62DX/audioai/performance.rst | 19 ++-- source/images/audioai/export-diagrams.sh | 18 ++++ source/images/audioai/tvm_tidl_flow.drawio | 21 ++--- source/images/audioai/tvm_tidl_flow.png | Bin 127058 -> 43683 bytes source/images/audioai/tvm_tidl_flow.svg | 2 +- 8 files changed, 150 insertions(+), 99 deletions(-) create mode 100755 source/images/audioai/export-diagrams.sh diff --git a/source/devices/AM62DX/audioai/compile_evaluate_inspect.rst b/source/devices/AM62DX/audioai/compile_evaluate_inspect.rst index 0cde3f574..b2a3de826 100644 --- a/source/devices/AM62DX/audioai/compile_evaluate_inspect.rst +++ b/source/devices/AM62DX/audioai/compile_evaluate_inspect.rst @@ -8,11 +8,19 @@ Model Compile / Evaluate / Inspect The :ref:`Audio AI Model Zoo ` runs *pre-compiled* artifacts on the |__PART_FAMILY_NAME__| target. This section covers the **x86 host -workflow** that produces those artifacts: how to compile each audio model for -the C7x\ |tm| NPU, run host-emulation inference, and measure accuracy against a +workflow** that produces those artifacts: how to compile an audio model for +the C7\ |tm| NPU, run host-emulation inference, and measure accuracy against a dataset — using **edgeai-tidlrunner**. It runs entirely on an Ubuntu x86 PC; nothing here touches the target. +This is the workflow for *any* audio model, not just the reference models. The +four reference models below are worked examples; to bring your own model you +point the same ``tidlrunner-cli`` at your ONNX graph and a per-model YAML config, +using a reference config as a starting template. Which operators offload to the +C7\ |tm| NPU (vs. fall back to the Arm core) is determined by TIDL — see +`edgeai-tidl-tools `__ for +the authoritative list of supported operators. + ******************** edgeai-tidlrunner ******************** @@ -119,7 +127,7 @@ Interim AM62D TVM-RT compile path .. warning:: - The AM62D C7x\ |tm| compile path for the three TVM-RT models (VGGish11, + The AM62D C7\ |tm| NPU compile path for the three TVM-RT models (VGGish11, YAMNet, GCRN) is currently an **interim feasibility flow**, not the productized ``setup_runner_pc.sh`` / ``TIDL_TOOLS_VERSION`` download. It is documented here for reproducibility and will be replaced before release. @@ -130,8 +138,9 @@ wheel ships the x86 AM62D TIDL tools *inside* the package, so the AM62D compile uses the wheel-bundled tools rather than the ones ``setup_runner_pc.sh`` downloads. TVM ``dlopen``\ s the TIDL runtime before Python's own import runs, so the tool paths must be exported into the environment *before* ``tidlrunner-cli`` -starts. A small wrapper, ``agent_ws/am62d-run.sh``, does this and then runs the -command you pass it. It exports: +starts. A sourceable env script, ``devices/am62d_env.sh``, exports these into +the current shell once; every ``tidlrunner-cli`` command run afterward in that +shell picks them up. It exports: .. list-table:: :header-rows: 1 @@ -151,22 +160,22 @@ command you pass it. It exports: * - ``SOC`` - ``am62d`` -Activate the dedicated environment, then prefix each TVM-RT command with the -wrapper (the whole ``tidlrunner-cli`` invocation is passed as one quoted -argument): +Activate the dedicated environment, then source the env script once per shell +before running ``tidlrunner-cli`` directly: .. code-block:: console $ pyenv activate tidlrunner-am62d - $ bash agent_ws/am62d-run.sh "tidlrunner-cli compile --config_path " + $ source devices/am62d_env.sh + $ tidlrunner-cli compile --config_path .. note:: - GTCRN does **not** use this wrapper. It runs ARM-only through ONNX Runtime - (``tidl_offload: false``) and compiles under the standard ``tidlrunner`` - environment from steps 1–4. + GTCRN does **not** need this env script. It runs ARM-only through ONNX + Runtime (``tidl_offload: false``) and compiles under the standard + ``tidlrunner`` environment from steps 1–4. -.. TODO(WI-004): replace the interim ``agent_ws/am62d-run.sh`` wrapper + +.. TODO: replace the interim ``devices/am62d_env.sh`` env script + ``tidlrunner-am62d`` venv with the productized ``setup_runner_pc.sh`` / ``TIDL_TOOLS_VERSION`` AM62D flow before release. Confirm the final tools version string (artifact folder 11_02_18_00 vs. RC bundled tools 11.02.16.00). @@ -176,8 +185,11 @@ Compile and evaluate ******************** Each model has one config that drives all stages. Three models compile to the -C7x\ |tm| NPU through the TVM runtime; GTCRN is the ONNX-RT exception that runs -ARM-only. The final AM62D config paths are: +C7\ |tm| NPU through the TVM runtime; GTCRN is the ONNX-RT exception that runs +ARM-only. The three TVM-RT models below assume the AM62D env from +`Interim AM62D TVM-RT compile path`_ — ``pyenv activate tidlrunner-am62d`` +followed by ``source devices/am62d_env.sh`` — is already sourced in the +current shell. The final AM62D config paths are: .. list-table:: :header-rows: 1 @@ -212,14 +224,14 @@ ARM-only. The final AM62D config paths are: VGGish11 (TVM-RT, 8-bit) ======================== -Runs on the C7x\ |tm| NPU (``tidl_offload: true``). Use the AM62D wrapper: +Runs on the C7\ |tm| NPU (``tidl_offload: true``): .. code-block:: console $ CFG=data/configs/samples/models/audio/audio_classification/urbansound8k/vggish11_tvmrt_config.yaml - $ bash agent_ws/am62d-run.sh "tidlrunner-cli compile --config_path $CFG" - $ bash agent_ws/am62d-run.sh "tidlrunner-cli infer --config_path $CFG" - $ bash agent_ws/am62d-run.sh "tidlrunner-cli evaluate --config_path $CFG" + $ tidlrunner-cli compile --config_path $CFG + $ tidlrunner-cli infer --config_path $CFG + $ tidlrunner-cli evaluate --config_path $CFG ``evaluate`` reports top-1 / top-5 / macro-F1 over UrbanSound8K fold 10. @@ -229,22 +241,22 @@ YAMNet (TVM-RT, 8-bit) .. code-block:: console $ CFG=data/configs/samples/models/audio/audio_classification/urbansound8k/yamnet_tvmrt_config.yaml - $ bash agent_ws/am62d-run.sh "tidlrunner-cli compile --config_path $CFG" - $ bash agent_ws/am62d-run.sh "tidlrunner-cli infer --config_path $CFG" - $ bash agent_ws/am62d-run.sh "tidlrunner-cli evaluate --config_path $CFG" + $ tidlrunner-cli compile --config_path $CFG + $ tidlrunner-cli infer --config_path $CFG + $ tidlrunner-cli evaluate --config_path $CFG GCRN (TVM-RT, 16-bit) ===================== -GCRN is C7x\ |tm|-offloaded at 16-bit (``tidl_offload: true``, fixed 4-second -input). Same wrapper flow: +GCRN is C7\ |tm| NPU-offloaded at 16-bit (``tidl_offload: true``, fixed 4-second +input): .. code-block:: console $ CFG=data/configs/samples/models/audio/speech_enhancement/voicebank_demand_16k/gcrn_fixed_4sec_tvmrt_config.yaml - $ bash agent_ws/am62d-run.sh "tidlrunner-cli compile --config_path $CFG" - $ bash agent_ws/am62d-run.sh "tidlrunner-cli infer --config_path $CFG" - $ bash agent_ws/am62d-run.sh "tidlrunner-cli evaluate --config_path $CFG" + $ tidlrunner-cli compile --config_path $CFG + $ tidlrunner-cli infer --config_path $CFG + $ tidlrunner-cli evaluate --config_path $CFG ``evaluate`` reports PESQ / STOI / SI-SDR over the VoiceBank-DEMAND-16k test set. @@ -252,7 +264,7 @@ GTCRN (ONNX-RT, ARM-only) ========================= GTCRN has a dynamic time axis and TIDL-unsupported operators, so it runs on the -Arm Cortex-A cores through ONNX Runtime (``tidl_offload: false``) — no C7x\ |tm| +Arm Cortex-A cores through ONNX Runtime (``tidl_offload: false``) — no C7\ |tm| NPU offload and **no** AM62D wrapper. Run it directly under the standard ``tidlrunner`` environment: @@ -266,10 +278,7 @@ offload and **no** AM62D wrapper. Run it directly under the standard .. note:: - The shipped ``gtcrn_dns3_config.yaml`` sets ``target_device: AM62A``; because - the model is ARM-only, execution is device-independent. Passing - ``--target_device AM62D`` keeps its work directory under the AM62D tree, - consistent with the other three models. + ******************** Model Inspector @@ -287,7 +296,7 @@ separately. The report opens in any browser and lands at: The report has three tabs: -- **Model Summary** — acceleration ratio (layers on the C7x\ |tm| DSP vs. Arm +- **Model Summary** — acceleration ratio (layers on the C7x DSP vs. Arm fallback), input/output specs, and an interactive ONNX graph. - **Subgraphs** — the TIDL subgraph(s) offloaded to the DSP, with per-layer identity and a quantization analysis (INT8 vs. FP32 activation histograms and @@ -305,16 +314,20 @@ GCRN showcase — an offloaded DSP subgraph ========================================= Because GCRN now compiles with ``tidl_offload: true`` on AM62D, its inspector -report shows a **real C7x\ |tm| DSP subgraph** rather than an all-Arm fallback: +report shows a **real C7x DSP subgraph** rather than an all-Arm fallback: the Subgraphs tab lists a single offloaded subgraph (16-bit) with its layer mapping, and the Performance tab breaks that subgraph down by cycles and memory. -Generate it through the AM62D wrapper: +Generate it with the AM62D env sourced (see +`Interim AM62D TVM-RT compile path`_): .. code-block:: console $ CFG=data/configs/samples/models/audio/speech_enhancement/voicebank_demand_16k/gcrn_fixed_4sec_tvmrt_config.yaml - $ bash agent_ws/am62d-run.sh "tidlrunner-cli inspect --config_path $CFG" + $ tidlrunner-cli inspect --config_path $CFG For contrast, running ``inspect`` on the GTCRN (ONNX-RT) config produces a report with **no** DSP subgraph — every layer is Arm fallback — which is what a -non-offloaded model looks like in the inspector. +non-offloaded model looks like in the inspector. GTCRN also runs with +``tidl_offload: false``, so its work directory uses a ``notidl`` segment in +place of the ```` shown in the path template above, e.g. +``work_dirs/compile/AM62D/notidl//...``. diff --git a/source/devices/AM62DX/audioai/introduction.rst b/source/devices/AM62DX/audioai/introduction.rst index a8ba7b25e..2fb7c3fe1 100644 --- a/source/devices/AM62DX/audioai/introduction.rst +++ b/source/devices/AM62DX/audioai/introduction.rst @@ -6,11 +6,17 @@ Introduction & Overview .. |tm| unicode:: U+2122 -This guide documents the audio deep-learning (Audio AI/DL) models supported on -the |__PART_FAMILY_NAME__| platform in |__SDK_FULL_NAME__| |__AUDIOAI_SDK_VERSION__|. -It is written for embedded developers who want to run, compile, evaluate, and -benchmark the supported audio models on the AM62D — either out of the box on the -target with pre-compiled artifacts, or through the x86 compile/evaluate workflow. +This guide walks through the workflow for developing and deploying audio +deep-learning (Audio AI/DL) models on the |__PART_FAMILY_NAME__| platform, using +|__SDK_FULL_NAME__| |__AUDIOAI_SDK_VERSION__|. It is written for embedded +developers who want to run, compile, evaluate, and benchmark audio models on the +AM62D — either out of the box on the target with pre-compiled artifacts, or +through the x86 compile/evaluate workflow. + +Four **reference models** serve as worked examples throughout the guide, but the +workflow is not limited to them: the same compile/evaluate/inspect flow is a +bring-your-own-model (BYOM) flow that accepts any supported `ONNX +`__ model. More reference models will be added over time. The remaining sections cover: @@ -18,20 +24,37 @@ The remaining sections cover: target (inference scripts and Jupyter notebooks) using pre-trained models and pre-compiled artifacts. - :ref:`Compile / Evaluate / Inspect ` — the - x86 workflow built on ``edgeai-tidlrunner``: compile each model for the AM62D, + x86 BYOM workflow built on ``edgeai-tidlrunner``: compile a model for the AM62D, evaluate accuracy on a dataset, and inspect the compiled subgraphs. - :ref:`Performance benchmark ` — latency and memory methodology with per-model tables. ******************** -Supported models +Development workflow ******************** -Four models are supported, in two task families. Every model is delivered as an -`ONNX `__ graph and, except for GTCRN, is offloaded to the -C7x\ |tm| NPU through the TVM runtime (TVM-RT); GTCRN is the one model that runs -ARM-only. See :ref:`the framework overview ` below for what -that means. +The end-to-end path is the same for a reference model or your own model: + +#. **Obtain an ONNX model** — a reference graph shipped with the AudioAI ModelZoo, + or your own exported ONNX. +#. **Compile** it for the C7\ |tm| NPU on an x86 host (:ref:`Compile / Evaluate / + Inspect `). +#. **Evaluate** accuracy or audio-quality metrics on a dataset. +#. **Inspect** the compiled subgraphs and per-layer analysis. +#. **Run** on the AM62D target using the compiled artifact + (:ref:`Audio AI Model Zoo `). +#. **Benchmark** latency and memory (:ref:`Performance `). + +******************** +Reference models +******************** + +Four reference models are provided across two task families, as worked examples +of the workflow. Each is delivered as an `ONNX `__ graph and, +except for GTCRN, is offloaded to the C7\ |tm| NPU through the TVM runtime +(TVM-RT); GTCRN is the one that runs ARM-only. See :ref:`the framework overview +` below for what that means. The same flow accepts your own +ONNX model, and more reference models will be added over time. Audio analysis and classification ================================== @@ -39,12 +62,12 @@ Audio analysis and classification **VGGish11** (model ID |__MODEL_ID_VGGISH11__|) Audio-clip classification. Input is a log-mel patch of shape ``(1, 1, 64, 126)``; output is ``(1, 10)`` class logits plus a ``(1, 512)`` embedding. Runs at 8-bit, - fully offloaded to the C7x\ |tm| NPU via TVM-RT (a single TIDL subgraph). + fully offloaded to the C7\ |tm| NPU via TVM-RT (a single TIDL subgraph). **YAMNet** (model ID |__MODEL_ID_YAMNET__|) Audio-event classification. Input is a log-mel patch of shape ``(1, 1, 96, 64)``; output is ``(1, 521)`` class scores. Runs at 8-bit, fully offloaded to the - C7x\ |tm| NPU via TVM-RT (a single TIDL subgraph). + C7\ |tm| NPU via TVM-RT (a single TIDL subgraph). Both classification models are evaluated on the |__DATASET_AUDIO_CLS__| dataset. @@ -54,7 +77,7 @@ Speech enhancement **GCRN** (model ID |__MODEL_ID_GCRN__|) Convolutional-recurrent speech enhancement (denoising) over a fixed 4-second window. Input and output are complex spectrograms of shape ``(1, 2, 401, 161)``. - Runs at 16-bit and is offloaded to the C7x\ |tm| NPU via TVM-RT + Runs at 16-bit and is offloaded to the C7\ |tm| NPU via TVM-RT (``tidl_offload: true``). **GTCRN** (model ID |__MODEL_ID_GTCRN__|) @@ -62,7 +85,7 @@ Speech enhancement are complex spectrograms of shape ``(1, 257, T, 2)`` with a dynamic time dimension ``T``. Runs FP32 on the Arm core through the ONNX runtime (``tidl_offload: false``) — it is the only supported model that is **not** - offloaded to the C7x\ |tm| NPU. + offloaded to the C7\ |tm| NPU. Both speech-enhancement models are evaluated on the |__DATASET_SPEECH_ENH__| dataset. @@ -74,27 +97,31 @@ TVM + TIDL framework ************************** The AM62D audio models are compiled and deployed with **TI TVM**, TI's fork of -the `Apache TVM `__ deep-learning compiler. TI TVM -integrates **TIDL** (TI Deep Learning) as an accelerator backend so that the -layers TIDL supports execute with optimized C7x\ |tm| NPU performance, while any -remaining layers are covered by TVM's own C7x\ |tm| NPU code generation. The -result is a single deployable artifact in which the **entire model runs on the -C7x**\ |tm| **NPU**. +the `Apache TVM `__ deep-learning compiler. The target +accelerator is the **C7**\ |tm| **NPU** — TI's Neural Processing Unit, which +combines the **C7x DSP** with the **Matrix Multiplication Accelerator (MMA)** +(referred to as *C7x+MMA* in older documentation). + +TI TVM integrates **TIDL** (TI Deep Learning) as an accelerator backend: +TIDL-supported layers are offloaded to the MMA, while TVM generates **C7x DSP** +code for any remaining layers. Because the MMA and the C7x DSP together make up +the C7\ |tm| NPU, the result is a single deployable artifact in which the +**entire model runs on the C7**\ |tm| **NPU**. Compilation runs on an x86 host and proceeds in stages: #. **Model import** — the ONNX graph is imported into TVM. #. **Partitioning** — using the TIDL import API, the graph is split into TIDL-supported subgraphs (compiled by the TIDL network compiler) and the - remaining operators (handled by TVM C7x\ |tm| NPU code generation). + remaining operators (handled by TVM C7x DSP code generation). #. **Artifact generation** — both parts are packaged into one TVM-RT deployable artifact (``deploy_lib.so`` + ``deploy_graph.json`` + ``deploy_param.params``). At run time on the AM62D, the TVM runtime loads the artifact and dispatches the -offloaded subgraphs to TIDL-RT on the C7x\ |tm| NPU. +offloaded subgraphs to TIDL-RT on the C7\ |tm| NPU. -.. figure:: ../../../images/audioai/tvm_tidl_flow.png - :scale: 55 % +.. figure:: ../../../images/audioai/tvm_tidl_flow.svg + :width: 80 % :align: center TVM + TIDL compile-and-deploy flow for the AM62D audio models. Green blocks diff --git a/source/devices/AM62DX/audioai/model_zoo.rst b/source/devices/AM62DX/audioai/model_zoo.rst index 337ab1918..401a589c5 100644 --- a/source/devices/AM62DX/audioai/model_zoo.rst +++ b/source/devices/AM62DX/audioai/model_zoo.rst @@ -7,13 +7,14 @@ Audio AI Model Zoo .. |tm| unicode:: U+2122 The AudioAI ModelZoo delivers out-of-box audio deep-learning demos that run -directly on the |__PART_FAMILY_NAME__| target. Each supported model ships as an +directly on the |__PART_FAMILY_NAME__| target. Each reference model ships as an `ONNX `__ graph plus, where applicable, a pre-compiled -TVM-RT + TIDL artifact, so you can reproduce inference on the AM62D without +TVM-RT + TIDL artifact, so you can reproduce these examples on the AM62D without compiling anything yourself. Two demo front-ends are provided: standalone -Python inference scripts and Jupyter notebooks. This section runs entirely -**on the target**; the x86 compile/evaluate flow is covered in -:ref:`Compile / Evaluate / Inspect `. +Python inference scripts and Jupyter notebooks. The same on-target runtime hosts +your own compiled artifacts once you build them through the x86 flow. This +section runs entirely **on the target**; the x86 compile/evaluate flow is covered +in :ref:`Compile / Evaluate / Inspect `. .. note:: @@ -36,17 +37,17 @@ What ships * - VGGish11 - Sound classification (|__DATASET_AUDIO_CLS__|) - |__MODEL_ID_VGGISH11__| - - C7x\ |tm| NPU (TVM-RT + TIDL, 8-bit) + - C7\ |tm| NPU (TVM-RT + TIDL, 8-bit) - Script + notebook * - YAMNet - Sound classification (|__DATASET_AUDIO_CLS__|) - |__MODEL_ID_YAMNET__| - - C7x\ |tm| NPU (TVM-RT + TIDL, 8-bit) + - C7\ |tm| NPU (TVM-RT + TIDL, 8-bit) - Script + notebook * - GCRN - Speech enhancement (|__DATASET_SPEECH_ENH__|) - |__MODEL_ID_GCRN__| - - C7x\ |tm| NPU (TVM-RT + TIDL, 16-bit) + - C7\ |tm| NPU (TVM-RT + TIDL, 16-bit) - Script + notebook * - GTCRN - Speech enhancement (|__DATASET_SPEECH_ENH__|) @@ -55,7 +56,7 @@ What ships - Notebook only VGGish11, YAMNet, and GCRN each ship a pre-compiled artifact and run offloaded -to the C7x\ |tm| NPU through the TVM runtime. GTCRN ships **no** compiled +to the C7\ |tm| NPU through the TVM runtime. GTCRN ships **no** compiled artifact — it runs FP32 on the Arm core through the ONNX runtime and is demonstrated in a notebook only. @@ -221,8 +222,8 @@ the destination wav. Expected output (abridged): Jupyter notebook demos ********************** -All four models — including GTCRN — have a Jupyter notebook demo. Start Jupyter -Lab from the activated venv: +All four reference models — including GTCRN — have a Jupyter notebook demo. Start +Jupyter Lab from the activated venv: .. code-block:: console diff --git a/source/devices/AM62DX/audioai/performance.rst b/source/devices/AM62DX/audioai/performance.rst index 67fd1355d..5df57681b 100644 --- a/source/devices/AM62DX/audioai/performance.rst +++ b/source/devices/AM62DX/audioai/performance.rst @@ -7,10 +7,11 @@ Performance Benchmark .. |tm| unicode:: U+2122 This section reports **inference performance** — latency and memory — for the -four |__PART_FAMILY_NAME__| Audio AI models. It is **not** an accuracy or -audio-quality benchmark: model quality (classification accuracy, PESQ / STOI / -SI-SDR) is measured by the ``evaluate`` step in -:ref:`Compile / Evaluate / Inspect `. +reference |__PART_FAMILY_NAME__| Audio AI models. The same methodology (perfsim +estimate + on-target measured) applies to any model you compile through the +workflow. It is **not** an accuracy or audio-quality benchmark: model quality +(classification accuracy, PESQ / STOI / SI-SDR) is measured by the ``evaluate`` +step in :ref:`Compile / Evaluate / Inspect `. *********** Methodology @@ -29,7 +30,7 @@ confused: - Produced by the TIDL performance simulator during ``compile``. The compiler writes a ``result.yaml`` per model under its work directory; the ``perfsim`` latency, GMAC count, and estimated DDR transfer are - read directly from it. This is a *static estimate* for the C7x\ |tm| + read directly from it. This is a *static estimate* for the C7\ |tm| NPU subgraph — it does not include Arm-side pre/post-processing or runtime overhead. * - **on-target measured** @@ -39,7 +40,7 @@ confused: **real-time factor (RTF)** = processing time ÷ audio duration; RTF < 1 means the model keeps up with real-time audio. -Only models compiled for the C7x\ |tm| NPU (TVM-RT + TIDL) have a perfsim +Only models compiled for the C7\ |tm| NPU (TVM-RT + TIDL) have a perfsim estimate. GTCRN runs FP32 on the Arm core through the ONNX runtime and has no TIDL subgraph, so perfsim does not apply to it. @@ -105,15 +106,15 @@ filled in from the Model Zoo inference scripts on the AM62D EVM: - Measured latency - RTF * - VGGish11 - - C7x\ |tm| NPU + - C7\ |tm| NPU - TBD - — (clip classifier) * - YAMNet - - C7x\ |tm| NPU + - C7\ |tm| NPU - TBD - — (clip classifier) * - GCRN - - C7x\ |tm| NPU + - C7\ |tm| NPU - TBD - TBD * - GTCRN diff --git a/source/images/audioai/export-diagrams.sh b/source/images/audioai/export-diagrams.sh new file mode 100755 index 000000000..4638f5f65 --- /dev/null +++ b/source/images/audioai/export-diagrams.sh @@ -0,0 +1,18 @@ +#!/usr/bin/env bash +# Re-export every .drawio source in this directory to .png and .svg. +# Run after editing any .drawio file here. +# +# --svg-theme light is required: drawio's default (auto) emits CSS +# light-dark()/color-scheme, which makes the diagram invert colors +# under a dark OS/browser theme. Sphinx has no dark-mode support, so +# the exported SVG must stay pinned to the light palette. +set -euo pipefail +cd "$(dirname "$0")" + +DRAWIO=$(command -v drawio || echo /opt/drawio/drawio) + +for src in *.drawio; do + base="${src%.drawio}" + "$DRAWIO" --export --format png --embed-diagram --output "${base}.png" "$src" + "$DRAWIO" --export --format svg --svg-theme light --embed-diagram --output "${base}.svg" "$src" +done diff --git a/source/images/audioai/tvm_tidl_flow.drawio b/source/images/audioai/tvm_tidl_flow.drawio index 2c5246eb2..6f99ee07a 100644 --- a/source/images/audioai/tvm_tidl_flow.drawio +++ b/source/images/audioai/tvm_tidl_flow.drawio @@ -3,7 +3,7 @@ - + @@ -19,7 +19,7 @@ - + @@ -45,17 +45,14 @@ - + - + - - - - - + + @@ -82,11 +79,5 @@ - - - - - - diff --git a/source/images/audioai/tvm_tidl_flow.png b/source/images/audioai/tvm_tidl_flow.png index a5a7d3b8c05b6cd3c61d832cdefa8d0e2506c88d..d528941f475fee6d950c2ac125defb4e23f05d35 100644 GIT binary patch literal 43683 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