Modular building blocks for mapping spiking neural networks onto a programmable neuromorphic processor

Chenglong Zou, Xiaoxin Cui, Guang Chen, Shuo Feng, Kefei Liu, Xinan Wang, Yuan Wang

Abstract: In the past few years, brain-inspired artificial intelligence (AI) system with energy-efficient neuromorphic computing has gathered lots of interests. However, how to build an efficient algorithm-to-hardware development tool for spiking neural networks (SNNs) remains a big challenge. In this paper, we present a novel design of neural controller which can flexibly generate the store-and-release signals in the designed SNNs. Further, we propose a mapping methodology with modular building blocks to deploy SNN models onto a many-core programmable neuromorphic processor. Experimental results show that the presented mapping method can be adaptive to various SNN layers of different sizes and quantization precisions. Besides, our demonstrated system on chip can achieve about 181 and 26 images per second runtime inference speed on MNIST and CIFAR-10 dataset respectively and show comparable accuracies and significantly better power performance with their artificial neural network (ANN) counterparts running on traditional GPU.