Quantizing TinyML Neural Models for ARM Cortex-M4 NPU Inference

On-Device TinyML Requirements

Privacy and zero-latency require that no telemetry audio or thermal frame buffers leave the NIH-01 hardware hub. To achieve this, we quantized a 14-layer biophysical transformer model down to INT8 precision using TensorFlow Lite for Microcontrollers.

Running on an ARM Cortex-M4 NPU clocked at 120MHz, inference executes at <45ms per sensor sampling frame while consuming under 18mW of power.