Unearthing the Potential of Spiking Neural Networks

Sayeed Shafayet Chowdhury, Adarsh Kumar Kosta, Deepika Sharma, Marco Paul E. Apolinario, Kaushik Roy

Abstract: Spiking neural networks (SNNs) offer a promising alternative to traditional analog neural networks (ANNs), especially for sequential tasks, with enhanced energy efficiency. The internal memory in SNNs obtained through the membrane potential equips them with innate lightweight temporal processing capabilities. However, the unique advantages of this temporal dimension of SNN s have not yet been effectively harnessed. To that end, this article delves deeper into the what, why and where of SNNs. By considering event-based optical flow as an exemplary task in vision-based navigation, we highlight that the true potential of SNNs lies in sequential tasks. The event-driven recurrent dynamics of a spiking neuron merged harmoniously with event camera inputs enables SNNs to outperform corresponding ANNs with a lower number of parameters for optical flow. Furthermore, we demonstrate that SNNs can be synergistically combined with ANNs to form SNN-ANN hybrids to obtain the best of both worlds in terms of accuracy, energy, memory, and training efficiency. Additionally’ the emergence of various near-memory and in-memory computing techniques has propelled efficient implementation of these approaches. Overall, the immediate future of SNNs looks exciting, as we discover the niche of SNN s, comprising sequential tasks with low power requirements.