Cortex M

Cortex-M (CMSIS-NN) Backend

Architecture

Not a delegate backend — no partitioner. Custom ops and graph passes replace ATen quantized ops with CMSIS-NN equivalents at the graph level.

Pipeline

Uses standard PT2E quantization (prepare_pt2e / convert_pt2e), then CortexMPassManager rewrites quantized ops to cortex_m:: equivalents.

from executorch.backends.cortex_m.quantizer.quantizer import CortexMQuantizer
from executorch.backends.cortex_m.passes.cortex_m_pass_manager import CortexMPassManager
from executorch.backends.cortex_m.edge_compile_config import cortex_m_edge_compile_config
from torch.export import export
from torchao.quantization.pt2e.quantize_pt2e import convert_pt2e, prepare_pt2e
from executorch.exir import to_edge_transform_and_lower

quantizer = CortexMQuantizer()
captured = export(model, example_inputs).module()
prepared = prepare_pt2e(captured, quantizer)
prepared(*example_inputs)  # calibration
quantized = convert_pt2e(prepared)

exported = export(quantized, example_inputs)
# The backend's own config: linear and the activations must survive to_edge or the
# lowering either fails or silently falls back to portable float kernels.
edge = to_edge_transform_and_lower(
    exported,
    compile_config=cortex_m_edge_compile_config(),
)
edge._edge_programs["forward"] = CortexMPassManager(
    edge.exported_program(), CortexMPassManager.pass_list
).transform()
et_program = edge.to_executorch()

In tests, CortexMTester wraps this pipeline:

from executorch.backends.cortex_m.test.tester import CortexMTester

tester = CortexMTester(model, example_inputs)
tester.quantize().export().to_edge().run_passes().to_executorch()

Key Files

C++ kernels calling CMSIS-NN APIs live under backends/cortex_m/ops/.

Testing

Toolchain setup (required for test_implementation tests):

./examples/arm/setup.sh --i-agree-to-the-contained-eula
source ./examples/arm/arm-scratch/setup_path.sh

Run all tests:

source ./examples/arm/arm-scratch/setup_path.sh
pytest backends/cortex_m/test/

test_dialect_* tests verify graph correctness (pure Python, no toolchain needed). test_implementation_* tests verify numerical accuracy on the Corstone-300 FVP (requires toolchain on PATH).

Baremetal build:

backends/cortex_m/test/build_test_runner.sh

Adding a New Op

1. Define the op schema, meta function, and reference implementation in operators.py
2. Write the C++ kernel in backends/cortex_m/ops/ calling CMSIS-NN APIs
3. Register the .out kernel in operators.yaml
4. Add a pass to rewrite the ATen op → cortex_m:: op
5. Test with CortexMTester.test_dialect() (graph correctness) and test_implementation() (numerical accuracy on FVP)