LAUSR

Neuromorphic computing is considered to be the future of machine learning, and it provides a new way of cognitive computing. Inspired by the excellent performance of spiking neural networks (SNNs) on the fields of low-power consumption and parallel computing, many groups tried to simulate the SNN with the hardware platform. However, the efficiency of training SNNs with neuromorphic algorithms is not ideal enough. Facing this, Michael et al . proposed a method which can solve the problem with the help of DNN (deep neural network). With this method, we can easily convert a well-trained DNN into an SCNN (spiking convolutional neural network). So far, there is a little of work focusing on the hardware accelerating of SCNN. The motivation of this paper is to design an SNN processor to accelerate SNN inference for SNNs obtained by this DNN-to-SNN method. We propose SIES (Spiking Neural Network Inference Engine for SCNN Accelerating). It uses a systolic array to accomplish the task of membrane potential increments computation. It integrates an optional hardware module of max-pooling to reduce additional data moving between the host and the SIES. We also design a hardware data setup mechanism for the convolutional layer on the SIES with which we can minimize the time of input spikes preparing. We implement the SIES on FPGA XCVU440. The number of neurons it supports is up to 4 000 while the synapses are 256 000. The SIES can run with the working frequency of 200 MHz, and its peak performance is 1.562 5 TOPS.