Algorithm for Mapping Multilayer BP Networks onto the SpiNNaker Neuromorphic Hardware

Abstract

This paper demonstrates the feasibility and evaluates the performance of using the SpiNNaker neuromorphic hardware to simulate traditional non-spiking multi-layer perceptron networks with the back propagation learning rule. In addition to investigating the mapping of checker-boarding partitioning scheme onto SpiNNaker, we propose a new algorithm called pipelined checker-boarding partitioning which introduces a pipelined mode and captures the parallelism within each partition of the weight matrix, allowing the overlapping of communication and computation. Not only does the proposed algorithm localize communication, but it can also hide a part of or even all the communication. The performance is evaluated with SpiNNaker configurations up to 1000 nodes (20000 cores).