Edge AI Inference: A Deep Dive from Software to Hardware Acceleration

This article delves into the challenges and opportunities of running AI inference on resource-constrained microcontrollers. Starting with the mechanics of TensorFlow Lite Micro, the author analyzes the software implementation and hardware acceleration schemes based on ARM architecture extensions for the addition operator. The article also covers utilizing Arm's Ethos-U NPU for model acceleration. It reveals how different hardware architectures impact AI inference performance and how software and hardware optimizations can be combined to improve efficiency.