An FPGA-Based Co-Processor for Spiking Neural Networks with On-Chip STDP-Based Learning

Abstract

In this paper, we report on the design of a neuromorphic co-processor on a Field Programmable Gate Array (FPGA) platform that is capable of emulating Spiking Neural Networks (SNNs) with support for on-chip unsupervised learning. One defining feature of our design is that the SNN configuration is defined entirely in the software executed by our neuromorphic co-processor. Evaluation on the FPGA platform shows that our design consumes a small amount of hardware resources and on-chip memory storage (438.75 kB). In addition, the inference and the on-chip learning in a deep convolutional SNN emulated on our FPGA implementation are $10.5vf \times$ and $8.6 \times$ faster than the implementation on high performance x86 CPU. Moreover, we demonstrate the ability of our neuromorphic co-processor to perform the on-chip learning on an object recognition task (based on the Caltech-101 dataset).