Performance Analysis of Spiking Neural Network using Temporal Spike-based Backpropagation on Field Programmable Gate Array (FPGA) platform

Abstract

This paper explores the object classification performance of spiking neural networks (SNNs) using the temporal spike-based backpropagation technique on the Field Programmable Gate Array (FPGA) platform. The FPGA board is specially designed to host the spiking neural networks for artificial intelligence tasks such as object classification, object detection, and segmentation. The publicly available classification datasets such as MNIST, CIFAR10 were employed to examine the performance of the SNNs on the FPGA platform. Similarly, the latest temporal spike-based backpropagation technique was chosen to investigate the neuromorphic ability of the low-cost FPGA board in processing SNNs for object classification tasks. The main purpose of this research proceeding is to facilitate the neuromorphic research community with the information regarding (i). the exploitation of the low-cost FPGA design for neuromorphic image processing and artificial intelligence (AI) tasks; (ii). cross-validating temporal spike-based backpropagation trained SNNs on FPGA alongside PC; (iii). assessing the performance stability and industrial choices of low-cost FPGAs for object classification tasks and related issues. The evaluation metrics such as classification accuracy, mean average precision, and processing time were utilized to assess the performance of the SNN model on FPGA alongside PC. This study will be used as an informative report for the researchers working towards perfecting the neuromorphic hardware for processing SNNs in imminent studies.