Training Large-Scale Spiking Neural Networks on Multi-core Neuromorphic System Using Backpropagation

Abstract

Neuromorphic circuits with nonvolatile memory crossbar arrays can train and inference neural networks in a highly power-efficient manner, which can be a solution to overcome the von Neumann bottleneck. This paper proposes a scalable multi-core spiking neuromorphic system architecture that can support a large-scale multi-layer neural network larger than a network supported by a computing system with a single neuromorphic circuit core. To simplify the inter-core communication, neuromorphic cores communicate only by sending and receiving spikes. Deep networks can be easily formed on this architecture by connecting multiple cores. The neuromorphic cores are trained on-chip by backpropagation, which is a well-known and sophisticated algorithm for training neural networks in software. We made modifications to the traditional backpropagation algorithm to propagate errors and update weights by spikes on the spiking neuromorphic cores of a computing system using our architecture. The proposed algorithm was evaluated by an spike event-based neuromorphic circuit simulator using three datasets. Cancer1 and Thyroid1 were used for a small network evaluation, which results showed better test error than previous studies, and MNIST was used to evaluate a large realistic neural network.