Neu-NoC: a high-efficient interconnection network for accelerated neuromorphic systems

A modern neuromorphic acceleration system could consist of hundreds of accelerators, which are often organized through a network-on-chip (NoC). Although the overall computing ability is greatly promoted by a large number of the accelerators, the power consumption and average delay of the NoC itself becomes prominent. In this paper, we first analyze the characteristics of the data traffic in neuromorphic acceleration systems and the bottleneck of the popular NoC designs adopted in such systems. We then propose Neu-NoC - a high-efficient interconnection network to reduce the redundant data traffic in neuromorphic acceleration systems and explore the data transfer ability between adjacent layers. A sophisticated neural network aware mapping algorithm and a multicast transmission scheme are designed to alleviate data traffic congestions without increasing the average transmission distance. Finally, we explore the sparsity characteristics of fully-connected NNs. Simulation results show that compared to the most widely-used Mesh NoC design, Neu-NoC can substantially reduce the average data latency by 28.5% and the energy consumption by 39.2% in accelerated neuromorphic systems.