Fine-grained Hardware Acceleration for Efficient Batteryless Intermittent Inference on the Edge

Abstract

Backing up the intermediate results of hardware-accelerated deep inference is crucial to ensure the progress of execution on batteryless computing platforms. However, hardware accelerators in low-power AI platforms only support the one-shot atomic execution of one neural network inference without any backups. This article introduces a new toolchain for MAX78000, which is a brand-new microcontroller with a hardware-based convolutional neural network (CNN) accelerator. Our toolchain converts any MAX78000-compatible neural network into an intermittently executable form. The toolchain enables finer checkpoint granularity on the MAX78000 CNN accelerator, allowing for backups of any intermediate neural network layer output. Based on the layer-by-layer CNN execution, we propose a new backup technique that performs only necessary (urgent) checkpoints. The method involves the batteryless system switching to ultra-low-power mode while charging, saving intermediate results only when input power is lower than ultra-low-power mode energy consumption. By avoiding unnecessary memory transfer, the proposed solution increases the inference throughput by 1.9× for simulation and by 1.2× for real-world setup compared to the coarse-grained baseline execution.