Reconfigurable platforms and the challenges for large-scale implementations of spiking neural networks

Abstract

FPGA devices have witnessed popularity in their use for the rapid prototyping of biological Spiking Neural Network (SNNs) applications, as they offer the key requirement of reconfigurability. However, FPGAs do not efficiently realise the biological neuron/synaptic models. Also their routing structures cannot accommodate the high levels of neuron inter-connectivity inherent in complex SNNs. This paper highlights and discusses the current challenges of implementing large scale SNNs on reconfigurable FPGAs. The paper presents a novel Field Programmable Neural Network (FPNN) architecture incorporating low power analogue synapse and a network on chip architecture for SNN routing and configuration. Initial results are presented.