REC

Abstract

As the Internet of Things (IoTs) increasingly combines AI technology, it is a trend to deploy neural network algorithms at edges and make IoT devices more intelligent than ever. Moreover, the energy harvesting technology-based IoT devices have shown the advantages of green economy, convenient maintenance, and theoretically infinite lifetime, etc. However, the harvested energy is often unstable, resulting in low performance due to the fact that a fixed load can't sufficiently utilize the harvested energy. To address this problem, recent works focusing on ReRAM-based convolutional neural networks (CNN) accelerators under harvested energy have proposed hardware/software optimizations. However, those works have overlooked the mismatch between the power requirement of different CNN layers and the variation of harvested power. Motivated by the above observation, this paper proposes a novel strategy, called REC, that retimes convolutional layers of CNN inferences to improve the performance and energy efficiency of energy harvesting ReRAM-based accelerators. Specifically, at the offline stage, REC defines different power levels to match the power requirements of different convolutional layers. At runtime, instead of sequentially executing the convolutional layers of a reference one by one, REC retimes the execution timeframe of different convolutional layers so as to accommodate different CNN layers to the changing power inputs. What is more, REC provides a parallel strategy to fully utilize very high power income. Our experimental results show that the proposed REC approach achieves an average performance improvement of 6.1x (up to 16.5x) compared to the traditional strategy without the REC idea.