Why TFLite Micro
TensorFlow Lite for Microcontrollers runs inference on devices with as little as 20KB RAM. On STM32H7 (1MB RAM, 480MHz Cortex-M7) you can run keyword spotting, gesture classification, and anomaly detection within tight power budgets.
INT8 Quantization
INT8 quantization reduces model size ~4x and inference latency ~2-3x with minimal accuracy loss. Use tf.lite.Optimize.DEFAULT with a representative calibration dataset to determine per-layer quantization scales.
CMSIS-NN Acceleration
ARM's CMSIS-NN provides optimized kernels using SIMD DSP extension instructions. TFLite Micro automatically uses CMSIS-NN when included. On STM32H7, a MobileNet-v1 (0.25x, 96x96) runs in ~18ms vs ~67ms without — a 3.7x speedup.
Deployment
constexpr int kTensorArenaSize = 80 * 1024;
uint8_t tensor_arena[kTensorArenaSize];
tflite::MicroInterpreter interpreter(
model, resolver, tensor_arena, kTensorArenaSize
);
interpreter.AllocateTensors();
interpreter.Invoke();Power Benchmarks
- STM32H7 @ 480MHz: ~18mW during inference, ~2mW idle
- STM32H7 @ 120MHz (power-optimized): ~6mW inference
- Duty cycle 10ms/500ms = ~0.36mW average — sub-5mW achievable