SiBrain: A Sparse Spatio-Temporal Parallel Neuromorphic Architecture for Accelerating Spiking Convolution Neural Networks With Low Latency

Currently, the performance of spiking neural networks (SNNs) under complicated tasks is gradually close to that of convolutional neural networks (CNNs). However, the existing neuromorphic computing hardware architectures (NCHAs) for accelerating SNNs lack effective sparse detection and cannot realize effective spatio-temporal parallel computing, resulting in high inference latency. In this paper, we propose an improved neuromorphic computing hardware architecture (called SiBrain) with high accuracy, low power and low latency, consisting of a sparse spatio-temporal parallel processing element (S2TP-PE) array for spike convolution and pooling computation and a fully-connected (FC) Core for spike FC computation. In the novel S2TP-PE array, a S2TP computation unit is designed by using time-step and channel parallel computation techniques to reduce the latency caused by multiple time-steps of SNN, and a sparse detection and response unit is presented based on channel-cache and block-multiplex to achieve the spike detection, response, and reuse. Combining the above kernel components, the SiBrain is built and implemented on Virtex-7 FPGA with 200MHz. The experimental results show that the SiBrain can effectively support the deployment of SCNN models with different sizes, and the deployed large-scale Spiking Visual-Geometry-Group (VGG) model can achieve the recognition accuracies of 90.25% and 66.97% on CIFAR-10 and CIFAR-100 with the power consumption of 1.5 W and the energy efficiency of 83 GSOPs/W. Compared with existing FPGA-based SNN accelerators, SiBrain has a maximum increase in inference speed of nearly 11 times and a maximum decrease in energy consumption of nearly 34 times, respectively.