Float and FP16

Compile a float32 graph and a float16 graph for the same target, and see what the FP16 platform gate does when the hardware cannot serve it. Float support is experimental; qualify your model, kernel build and target before deployment.

Field	Value
Model	one SQRT node, built in float32 and in float16
Target	apollo510_evb
Shows	Float kernel selection, the FP16 platform gate, and the ns-cmsis-nn build switches a float module needs
CI	Conversion and host compilation are declared in the examples CI job, both precisions

The models are built by make_model.py. Each is a small SQRT fixture with matching input/output dtype, so it isolates conversion’s float path without depending on a downloaded model. It is not a general test of mixed precision, quantized weights or every float operator.

Prerequisites

Use a repository checkout and activate its environment after uv sync --frozen --group ci. The model-building script imports the repository’s Python helpers; an isolated CLI installation alone is not enough. See Running examples.

Run it

./run.sh

The two configurations, config-float32.yaml and config-float16.yaml, select the corresponding model and retain separately named output modules. Nothing in the configuration selects a float kernel: the tensor dtypes do.

The platform gate

apollo510_evb declares both a single-precision FPU and the FP16 capability, so both conversions resolve. Ask for float16 on a target without it and the conversion stops before emitting anything:

helia-aot convert --model.path models/sqrt_float16.tflite --model.name sqrt_float16 \
  --module.path ./out --module.name gate --module.type cmake --platform.name apollo4p_evb

Error: f16 kernels require a Cortex-M55-class/FP16 platform; got 'apollo4p_evb'
(cpu=cortex-m4)
  hint: Target a Cortex-M55-class/FP16 platform, or re-export the model without
float16 tensors.

This is a separate negative check; run.sh performs the two positive conversions. FP32 does not use the FP16 platform gate, but still needs supported operator contracts and a compatible library/toolchain build. Neither conversion nor host compilation proves numerical SQRT behavior or device FP16 execution.

The build switch

The float kernel objects exist only if ns-cmsis-nn was built with them, so the switch belongs on the library rather than on the generated module. For the CMake and NSX packagings set ARM_NN_ENABLE_F32 or ARM_NN_ENABLE_F16 before adding the ns-cmsis-nn subdirectory; for Zephyr the symbols are CONFIG_NS_CMSIS_NN_ENABLE_F32 and CONFIG_NS_CMSIS_NN_ENABLE_F16 in the application’s prj.conf. The module asks the library what it was built with, and a mismatch fails at configure time rather than at run time.

Next

• Float support for the precision table, the hardware requirements and the mixed-graph rules.
• Operator catalog for which operators have float kernels and which kernel-library floor they need.