[Pico2] Add CMSIS-NN INT8 support and latency instrumentation

[psiddh]

Add INT8 quantized inference via CMSIS-NN kernels for Cortex-M33 on Pico2, alongside the existing FP32 portable path. This enables a direct FP32 vs INT8 comparison for MCU deployment benchmarking.

• Add export_mlp_mnist_cmsis.py: quantized export using CortexMQuantizer
• CMakeLists.txt: USE_CMSIS_NN and USE_SELECTIVE_BUILD options for flexible linking
• build_firmware_pico.sh: --cmsis flag, fix TARGET_CPU to cortex-m33+nofp, portable nproc, auto-detect ARM toolchain, remove unused cmake flags
• main.cpp: per-digit inference timing via time_us_32()

[pytorch-bot]

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⏳ 20 Pending, 1 Unrelated Failure

As of commit 50bdac5 with merge base 75c677f ():

FLAKY - The following job failed but was likely due to flakiness present on trunk:

• Test Vulkan Backend / test-vulkan / test-backend-linux (vulkan, operators) / linux-job (gh) (detected as infra flaky with no log or failing log classifier)

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[Copilot]

Pull request overview

Adds an INT8/CMSIS-NN accelerated inference path for the Pico2 (Cortex-M33) MNIST MLP demo, plus instrumentation to compare FP32 vs INT8 latency and memory usage during MCU benchmarking.

Changes:

• Add a new export script to produce an INT8-quantized (CMSIS-NN-targeting) .pte using CortexMQuantizer + Cortex-M passes.
• Add build-time switches for CMSIS-NN linking and (intended) selective-build linking in the Pico2 CMake + build script.
• Add per-digit inference timing and a latency summary; add a method allocator memory-usage printout.

Reviewed changes

Copilot reviewed 4 out of 4 changed files in this pull request and generated 4 comments.

File	Description
examples/raspberry_pi/pico2/main.cpp	Adds per-inference timing + latency summary and a memory usage printout after method load.
examples/raspberry_pi/pico2/export_mlp_mnist_cmsis.py	New INT8/CMSIS-NN export flow using Cortex-M quantization + optimization passes.
examples/raspberry_pi/pico2/build_firmware_pico.sh	Adds --cmsis flag, portable nproc, toolchain autodetect, and Pico CMake flag handling.
examples/raspberry_pi/pico2/CMakeLists.txt	Adds USE_CMSIS_NN / USE_SELECTIVE_BUILD options and conditional linking for CMSIS-NN vs portable kernels.

---

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[Copilot]

CortexMPassManager.transform() returns an ExportedProgram that is already in Edge dialect (it’s transforming edge_program.exported_program()). Re-wrapping that result with to_edge() is meant for ATen->Edge conversion and can re-run ATen->Edge passes on an Edge program. Instead, construct an EdgeProgramManager directly from the transformed Edge ExportedProgram (e.g., EdgeProgramManager(transformed_ep, compile_config=edge_config)) before calling to_executorch().

[Copilot]

The script enables ExecuTorch selective build via -DEXECUTORCH_SELECT_OPS_MODEL, but the Pico2 firmware CMake is never told to link the selective kernel library (USE_SELECTIVE_BUILD option added in CMakeLists.txt). As a result, the firmware still links the full portable_ops_lib under --whole-archive, negating the binary-size benefits of selective build. Consider propagating this automatically (e.g., add -DUSE_SELECTIVE_BUILD=ON when SELECT_OPS_FLAGS is set) so selective build works end-to-end.

Suggested change

	if [ -n "${SELECT_OPS_FLAGS:-}" ]; then
	PICO_CMAKE_FLAGS+=(-DUSE_SELECTIVE_BUILD=ON)
	fi

[Copilot]

The allocator "probe" uses method_allocator.allocate(1, 1), which advances the allocator cursor and permanently consumes a byte (plus any padding). On tight SRAM budgets this can change whether method loading / subsequent allocations succeed and also makes the reported usage slightly inconsistent with the post-probe state. Prefer querying usage without mutating the allocator (e.g., allocate(0, 1) to get the current cursor without consuming space, or add/ use an explicit "bytes_used" accessor if available).

Suggested change

	// Probe method allocator usage: try allocating 1 byte to find cur_ position
	void* probe = method_allocator.allocate(1, 1);
	// Probe method allocator usage without consuming memory.
	void* probe = method_allocator.allocate(0, 1);

[Copilot]

In the USE_CMSIS_NN + USE_SELECTIVE_BUILD branch, portable_ops_lib / executorch_selected_kernels are not linked. Even with CMSIS-NN accelerated int8 linear, this model still needs non-CMSIS ops like view/reshape (from the TinyMLPMNIST forward) to be registered, which typically come from portable_ops_lib or executorch_selected_kernels. Consider linking executorch_selected_kernels alongside cortex_m_ops_lib (or keep portable_ops_lib) so this configuration doesn’t hit OperatorMissing at runtime.

Suggested change

	# CMSIS-NN model uses only cortex_m:: ops, no portable ops needed.
	# Skip --whole-archive on portable_ops_lib to avoid pulling unused ops.
	message(STATUS "Selective build: CMSIS-NN only (no portable ops)")
	target_link_libraries(
	executorch_pico
	PRIVATE ${BAREMETAL_BUILD_DIR}/lib/libexecutorch.a
	${BAREMETAL_BUILD_DIR}/lib/libexecutorch_core.a
	-Wl,--whole-archive
	${BAREMETAL_BUILD_DIR}/lib/libcortex_m_ops_lib.a
	# CMSIS-NN accelerates supported kernels, but selective builds may still
	# require non-CMSIS operator registrations (for example view/reshape).
	# Link executorch_selected_kernels alongside cortex_m_ops_lib so the
	# model-required non-CMSIS ops are registered without pulling all portable ops.
	message(STATUS "Selective build: CMSIS-NN + executorch_selected_kernels")
	target_link_libraries(
	executorch_pico
	PRIVATE ${BAREMETAL_BUILD_DIR}/lib/libexecutorch.a
	${BAREMETAL_BUILD_DIR}/lib/libexecutorch_core.a
	-Wl,--whole-archive
	${BAREMETAL_BUILD_DIR}/lib/libcortex_m_ops_lib.a
	${BAREMETAL_BUILD_DIR}/lib/libexecutorch_selected_kernels.a