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  2. ExecuTorch

ExecuTorch

1.5.1
PyTorch

  • Pack Type

    Software Pack

ExecuTorch: PyTorch Edge Runtime for on-device AI inference on Arm Cortex-M processors.

ExecuTorch CMSIS Pack

Overview

ExecuTorch is the PyTorch Edge Runtime, enabling efficient on-device AI inference. This pack is the Cortex-M / bare-metal distribution.

This pack provides: - Core Runtime: Program loading and execution - Portable Operators: Platform-independent operator implementations - Quantized Operators: Optimized quantized inference - Ethos-U Backend: Hardware acceleration for Arm Ethos-U NPU - Cortex-M Backend: CMSIS-NN optimized operators

Getting Started

Basic Usage

  1. Add the pack to your csolution: ```yaml packs:

    • pack: PyTorch::ExecuTorch ```

  2. Add required components: ```yaml components: # Core runtime (always required)

    • component: Machine Learning:ExecuTorch:Runtime
    • component: Machine Learning:ExecuTorch:Kernel Utils

    # Backend (choose one or more). CMSIS component IDs use a single # colon between Cclass / Cgroup / Csub, with a space (not "::") # inside the Csub name. - component: Machine Learning:ExecuTorch:Backend EthosU # or - component: Machine Learning:ExecuTorch:Backend CortexM ```

  3. Include ExecuTorch headers in your code: cpp #include <executorch/runtime/executor/program.h> #include <executorch/runtime/executor/method.h>

Model Integration

Operators are included as individual CMSIS components, one per op_*.cpp. Add the ones your model uses:

components:
  - component: Machine Learning:ExecuTorch Operators:Portable add
  - component: Machine Learning:ExecuTorch Operators:Portable mul
  # ... other operators used by your model

You can also generate this list automatically: a Model Pack produced from your .pte file declares dependencies on the operators the model needs, and the toolchain resolves them against this pack's components.

Renamed Components

The CMSIS-Pack schema limits component names to 32 characters. Since pack version 1.4.1 three operator components therefore carry shortened names; projects written against the 1.4.0 pack must update them:

Up to 1.4.0 From 1.4.1
Portable max_pool2d_with_indices_backward Portable max_pool2d_indices_bwd
Cortex-M quantized_depthwise_conv2d Cortex-M quantized_dw_conv2d
Cortex-M quantized_transpose_conv2d Cortex-M quantized_tr_conv2d

Memory Requirements

Component Flash (approx) RAM (approx)
Runtime 50-100 KB 4-8 KB
Per Operator 1-10 KB minimal
Ethos-U Backend 20-40 KB 2-4 KB

Actual requirements depend on: - Selected operators - Model complexity - Tensor sizes

Compiler Support

Tested with arm-none-eabi-gcc 14.3 and Arm Compiler for Embedded (AC6) 6.23 on Cortex-M85. LLVM Embedded Toolchain for Arm is expected to work but was not exercised for this release.

AC6 projects must build with -ffp-mode=full. armclang otherwise assumes finite math, which breaks operators that depend on IEEE inf/NaN behaviour (isinf, isnan, gelu).

Dependencies

  • ARM::CMSIS (core headers)
  • ARM::CMSIS-NN (for Cortex-M backend)
  • ARM::ethos-u-core-driver (for Ethos-U backend)

CMSIS-NN 8.0.0 on Cortex-M55 and Cortex-M85

The ARM::CMSIS-NN 8.0.0 pack does not compile in its default configuration for cores with Helium floating-point support (MVE-F), such as Cortex-M55 and Cortex-M85. Four of its element-wise sources fail with implicit declaration of function 'arm_nn_clamp_mve_f32' (or ..._f16).

Until a fixed CMSIS-NN release is available, enable CMSIS-NN's float support in any project that selects the Cortex-M operators on such a core:

# in the *.csolution.yml or *.cproject.yml
define:
  - ARM_NN_ENABLE_F32: 1
  - ARM_NN_ENABLE_F16: 1

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