Emulating Spiking Neural Networks for edge detection on FPGA hardware

Abstract

Spiking neural networks (SNNs) are an emerging computing paradigm that attempt to model the biological functions of the human brain. However, as networks approach the biological scale with significantly large numbers of neurons, software simulations face the problem of scalability and increasing computation times. Thus, numerous researchers have targeted hardware implementations in an attempt to more closely replicate the parallel processing capabilities of biological networks. Reconfigurable hardware is seen as a particularly viable platform for attempting to replicate to some degree the natural plasticity and flexibility of the human brain. This paper presents a scalable FPGA based implementation approach that facilitates the accelerated emulation of large-scale SNNs. The approach is validated using a SNN-based edge detection application where an order of magnitude speed performance increase was observed in comparison to a software equivalent implementation.