Spiking Neural Network Architecture Design and Performance Exploration towards the Design of a Scalable Neuro-Inspired System for Complex Cognition Applications

Abstract

Research into artificial neural networks (ANNs) is inspired by how information is dynamically and massively processed by biological neurons. Conventional ANNs research has received a wide range of applications including automation, but there are still problems of timing, power consumption, and massive parallelism. Spiking neural networks (SNNs), being the third-generation of neural networks, has drawn attention from a greater number of researchers due to the timing concept, which defines its closeness to biological Spiking Neural Network (bio-SNN tested) functions. Spike timing plays an important role in every spiking neuron and proves computationally more plausible than other conventional ANNs. The real biological and distinct neuron timing and spike firing can be modelled artificially using neurodynamics and spike neuron models. The spike timing dependent plasticity (STDP) learning rule also incorporates timing concepts and is suitable for training SNNs which describes general plasticity rules that depend on the actual timing of pre- and postsynaptic spikes. This work presents a software implementation of an SNN based on the Leaky Integrate-and-Fire (LIF) neuron model and STDP learning algorithm. Also, we present a novel hardware design and architecture of a lightweight neuro-processing core (NPC) to be implemented in a packet-switched based neuro-inspired system, named NASH. The NASH architecture uses the LIF neuron model and reduced flit format size that solves the problems of timing and high-power consumption. Software evaluation shows that our network tested 94% accuracy with MNIST datasets of handwritten digits.