关键词: 神经形态计算, 脉冲神经网络（SNN）, 脉冲时间依赖可塑性（STDP）, FPGA集群, NEST仿真器

Abstract:

Spiking Neural Network（SNN）-based neuromorphic computing is considered to be a better way to solve artificial intelligence problems because of its working mechanism closer to that of the biological brain. However, how to meet the high performance, low power consumption, and adapt to the scaling needs is a challenging problem for neuromorphic computing systems. Based on the FPGA heterogeneous computing platform ZYNQ cluster, on the NEST-like brain simulator, the problems of Spike-Timing Dependent Plasticity（STDP） synaptic computation with high complexity, low parallelism, and large hardware resource occupation are mainly solved. The experimental results show that the designed method has an performance of 14.7 times that of the Xeon E5-2620 CPU on the 8-node ZYNQ 7030 cluster. In terms of energy efficiency, it is 51.6 times that of the Xeon E5-2620 CPU and 20.6 times that of the 8-node ARM Cortex-A9.

Key words: neuromorphic computing, Spiking Neuron Networks（SNN）, Spike?Timing Dependent Plasticity（STDP）, FPGA cluster, NEST simulator