A 62.45 TOPS/W Spike-Based Convolution Neural Network Accelerator with Spatiotemporal Parallel Data Flow and Sparsity Mechanism

Abstract

Convolutional neural networks (CNNs) have been widely used for image recognition and classification in recent years. Low energy consumption is crucial in the circuit design of edge devices, and data reuse is one method of reducing energy consumption. In addition, spiking neural networks (SNNs) are receiving increasing attention due to their low power use. However, the temporal characteristic of SNNs causes repeated data access at different time steps, leading to high energy consumption. In this paper, a spiked-based CNN accelerator that can support various inference time steps and models is proposed. Spatiotemporal parallel data flows are employed to reuse data from different time steps and, convolution operations are used to reduce energy consumption. Furthermore, the accelerator is designed for high sparsity and event driven SNNs. The synthesis achieves power efficiency of 62.45 TOPS/W and area efficiency of 7.58 TOPS/kmm2.