Topology-Aware Mapping of Spiking Neural Network to Neuromorphic Processor

Abstract

Neuromorphic processors, the new generation of brain-inspired non-von Neumann computing systems, are developed to better support the execution of spiking neural networks (SNNs). The neuromorphic processor typically consists of multiple cores and adopts the Network-on-Chip (NoC) as the communication framework. However, an unoptimized mapping of SNNs onto the neuromorphic processor results in lots of spike messages on NoC, which increases the energy consumption and spike latency on NoC. Addressing this problem, we present a fast toolchain, NeuToMa, to map SNNs onto the neuromorphic processor. NeuToMa exploits the global topology of SNNs and uses the group optimization strategy to partition SNNs into multiple clusters, significantly reducing the NoC traffic. Then, NeuToMa dispatches the clusters to neuromorphic cores, minimizing the average hop of spike messages and balancing the NoC workload. The experimental results show that compared with the state-of-the-art technique, NeuToMa reduces the spike latency and energy consumption by up to 55% and 86%, respectively.