Towards Fast and Accurate Object Detection in Bio-Inspired Spiking Neural Networks Through Bayesian Optimization

Despite recent developments in deep learning and their success in computer vision, model efficiency is increasingly becoming a vital factor for their deployment in various real-world applications. To provide a more effective form of computational capabilities, bio-inspired spiking neural networks (SNNs) have attracted considerable interest in recent years. SNNs are known as the third-generation of neural networks, which mimic how information is transmitted and processed in the human brain. SNNs enable sparse yet efficient information transmission through spike trains, leading to exceptional computational and energy efficiency. Nevertheless, critical challenges in SNNs to date are two-fold: (a) latency: the number of time steps required to achieve competitive results and (b) synaptic operations: the total number of spikes generated during inference. Many researchers have attempted to address these challenges, but their applications have been mainly limited to image classification. In this work, we present a threshold voltage balancing method for object detection in SNNs, which utilizes Bayesian optimization to find optimal threshold voltages in SNNs. We specifically design Bayesian optimization to consider important characteristics of SNNs such as latency and number of synaptic operations. Furthermore, we introduce two-phase threshold voltages to provide faster and more accurate object detection while providing high energy efficiency. According to experimental results, the proposed methods achieve the state-of-the-art object detection accuracy in SNNs, and converge 2x and 1.85x faster than conventional methods on PASCAL VOC and MS COCO, respectively. Moreover, the total number of synaptic operations is reduced by 40.33% and 45.31% on PASCAL VOC and MS COCO, respectively.