CMSIS-Executorch/cmsis-executorch.csolution.yml at main · Arm-Examples/CMSIS-Executorch · GitHub

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# Copyright 2026 Arm Limited and/or its affiliates.
# SPDX-License-Identifier: Apache-2.0
#
# A minimal ExecuTorch-on-Ethos-U85 example for the Corstone-320 (SSE-320) FVP.
# Three steps build it:
#
#   cbuild setup cmsis-executorch.csolution.yml --active SSE-320-U85
#   python create_ai_layer.py cmsis-executorch.cbuild-mlops.yml
#   cbuild cmsis-executorch.csolution.yml --active SSE-320-U85
#
# The `mlops:` node below makes `cbuild setup` emit the *.cbuild-mlops.yml;
# create_ai_layer.py reads the NPU and Vela settings from it, exports the model
# from PyTorch in the project's venv and writes the AI layer.
solution:
  created-for: CMSIS-Toolbox@2.14.1
  cdefault:
  compiler: AC6

  packs:
    # Pinned exactly: the pack's C++ runtime and the Python exporter pinned in
    # requirements.txt must be the same ExecuTorch version, or the .pte fails
    # to load. `cbuild setup --packs` fetches it from the public index.
    - pack: PyTorch::ExecuTorch@1.4.1
    - pack: ARM::CMSIS
    - pack: ARM::CMSIS-NN
    - pack: ARM::CMSIS-Compiler
    - pack: ARM::Cortex_DFP

  target-types:
    # Corstone-320 with an Ethos-U85 NPU, simulated on the Arm FVP.
    - type: SSE-320-U85
      board: SSE-320
      device: SSE-320-FVP
      variables:
        - Board-Layer: $SolutionDir()$/board/Corstone-320/Board-U85.clayer.yml
        - AI-Layer: $SolutionDir()$/ai_layer/ai_layer.clayer.yml
      target-set:
        - set:
          images:
            - project-context: cmsis-executorch.Debug
          debugger:
            name: Arm-FVP
            # The model is its own GDB server (plugins/GDBServer.so, MDK FVP
            # models 11.32.23+). fvp.sh resolves that plugin when VS Code was
            # not started from a `vcpkg activate` shell, line-buffers the
            # model's stdout so the debug adapter sees the "Listening" banner,
            # and on macOS runs the model in Docker (Arm ships no macOS build).
            # On Windows point this straight at FVP_Corstone_SSE-320.exe.
            model: ${workspaceFolder}/.vscode/fvp.sh
            config-file: board/Corstone-320/fvp_config.txt
            args: "--simlimit 60"

  misc:
    # CMSIS-Toolbox 2.14.1 passes -mfpu=fpv5-sp-d16 to Clang for the Cortex-M85,
    # a single-precision FPU on a core with a double-precision one and MVE;
    # LLVM crashes on some kernels with that combination. The later option
    # wins, so name the M85's FPU explicitly (newer toolboxes leave it to -mcpu).
    - for-compiler: CLANG
      C-CPP:
        - -mfpu=fp-armv8-fullfp16-d16
      ASM:
        - -mfpu=fp-armv8-fullfp16-d16

  build-types:
    - type: Debug
      debug: on
      optimize: debug

  projects:
    - project: ./cmsis-executorch.cproject.yml

  # MLOps information: exported to cmsis-executorch.cbuild-mlops.yml by
  # `cbuild setup ... --active SSE-320-U85`. Retarget the NPU here and
  # create_ai_layer.py picks it up on its next run.
  # https://open-cmsis-pack.github.io/cmsis-toolbox/build-overview/#mlops-information
  mlops:
    description: TinyCNN int8 image classifier for Ethos-U85
    npu:
      type: Ethos-U85
    vela:
      system: Ethos_U85_SYS_DRAM_Mid # system-config from the Vela config
      memory: Shared_Sram # memory-mode from the Vela config
    model:
      clayer: $AI-Layer$
      name: TinyCNN
    simulator:
      target: SSE-320-U85