A Scheduling Framework for Decomposable Kernels on Energy Harvesting IoT Edge Nodes

Abstract

With the growing popularity of the Internet of Things (IoTs), emerging applications demand that edge nodes provide higher computational capabilities and long operation times while requiring minimal maintenance. Ambient energy harvesting is a promising alternative to batteries, but only if the hardware and software are optimized for the intermittent nature of the power source. At the same time, many compute tasks in IoT workloads involve executing decomposable kernels that may have application-dependent accuracy requirements. In this work, we introduce a hardware-software co-optimization framework for such kernels that aim to achieve maximum forward progress while running on energy harvesting Non-Volatile Processors (NVP). Using this framework, we develop an FFT and a convolution accelerator that computes up to 3.2x faster, while consuming 5.4x less energy, compared to a baseline energy-harvesting system. With our accuracy-aware scheduling strategy, the approximate computing enabled by this framework delivers on average 6.2x energy reduction and 3.2x speedup by sacrificing minimal accuracy of up to 6.9%.