Comprehensive Analytic Performance Assessment and K-means based Multicast Routing Algorithm and Architecture for 3D-NoC of Spiking Neurons

Abstract

Spiking neural networks (SNNs) are artificial neural network models that more closely mimic biological neural networks. In addition to neuronal and synaptic state, SNNs incorporate the variant time scale into their computational model. Since each neuron in these networks is connected to thousands of others, high bandwidth is required. Moreover, since the spike times are used to encode information in SNN, very low communication latency is also needed. The 2D-NoC was used as a solution to provide a scalable interconnection fabric in large-scale parallel SNN systems. The 3D-ICs have also attracted a lot of attention as a potential solution to resolve the interconnect bottleneck. The combination of these two emerging technologies provides a new horizon for IC designs to satisfy the high requirements of low power and small footprint in emerging AI applications. In this work, we first present a comprehensive analytical model to analyze the performance of 3D mesh NoC over variants of different SNN topologies and communications protocols. Second, we present an architecture and a low-latency spike routing algorithm, named shortest path K-means based multicast (SP-KMCR), for three-dimensional NoC of spiking neurons (3DNoC-SNN). The proposed system was validated based on an RTL-level implementation, while area/power analysis was performed using 45nm CMOS technology.