MCUNet: Tiny Deep Learning on IoT Devices
Machine learning on tiny IoT devices based on microcontroller units (MCU) is appealing but challenging: the memory of microcontrollers is 2 3 orders of magnitude less even than mobile phones. We propose MCUNet, a framework that jointly designs the efficient neural architecture (TinyNAS) and the lightweight inference engine (TinyEngine) enabling ImageNet scale inference on microcontrollers. TinyNAS adopts a two stage neural architecture search approach that first optimizes the search space to fit the resource constraints, then specializes the network architecture in the optimized search space. TinyNAS can automatically handle diverse constraints (i.e. device, latency, energy, memory) under low search costs. TinyNAS is co designed with TinyEngine, a memory efficient inference library to expand the design space and fit a larger model. TinyEngine adapts the memory scheduling according to the overall network topology rather than layer wise optimization, reducing the memory usage by 2.7x, and accelerating the inference by 1.7 3.3x compared to TF Lite Micro and CMSIS NN. MCUNet is the first to achieves >70% ImageNet top1 accuracy on an off the shelf commercial microcontroller, using 3.6x less SRAM and 6.6x less Flash compared to quantized MobileNetV2 and ResNet 18. On visual&audio wake words tasks, MCUNet achieves state of the art accuracy and runs 2.4 3.4x faster than MobileNetV2 and ProxylessNAS based solutions with 2.2 2.6x smaller peak SRAM. Our study suggests that the era of always on tiny machine learning on IoT devices has arrived.
NeurIPS