Multi-Core ARM-Based Hardware-Accelerated Computation for Spiking Neural Networks

Abstract

Distributed edge computing platforms are of great significance for the implementation of brain-like computing research. Due to the limited power consumption and real-time requirements, hardware acceleration of computing units is a challenging task. Taking advantage of both scalable hardware framework and lower cost, this paper designs a multi-core distributed computing platform for spiking neural networks. Particularly, a shared memory partition structure is utilized to participate in hardware acceleration. Through the spike-queue-based synaptic mapping mechanism, each parallel computing unit deals with efficient point-to-point connections. In addition, this platform provides a basic community unit (BCU) that encapsulates a standard neuron model library and rich peripheral interfaces. With the support of GUI, users can quickly build large-scale systems. The experimental results show that a single BCU can accommodate more than 10,000 neurons updated in real time at a power consumption of 273.6mW. The extended BCU group is able to perform network dynamic simulations in the basal ganglia-thalamus plausible biological network composed of H-H neurons as well as MNIST dataset classification in the LIF network. The outstanding flexibility and real-time performance of the proposed hardware architecture provide great potential for embedded applications of neural computation.