Efficient CNN Inference on Ultra-Low-Power MCUs via Saturation-Aware Convolution

Abstract: Deploying lightweight CNN inference tasks on ultra-low-power MCUs is often not limited by space constraint, thanks to the compact size of models, yet inference latency is crucial for preserving energy. We reveal that quantized CNN inference on ultra-low-power MCUs executes unnecessary computations in neurons that produce saturated output values: often times, these neurons still produce the correct output value without fully completing the computation, since the neuron value is too extreme and is eventually systematically clamped at the boundaries allowed by the neuron. We show that with carefully designed condition checks, it is possible to identify and skip these unnecessary computations without impacting the neuron output. Based on this, we present saturation-aware convolution: an inference technique whereby computations in convolution kernels are executed in an altered order to induce earlier saturation, and saturation checks are inserted to omit unnecessary computations. We integrate our implementation into MCUNet's TinyEngine, the state-of-the-art neural network code generation and inference framework, and conduct experiments on a Cortex-M0+ MCU. The result based on 7 open-source CNN models displays up to 24% inference time saving, with strictly zero impact on neural network accuracy.