Live Demonstration: Face Recognition at The Edge Using Fast On-Chip Deep Learning Neuromorphic Chip

Abstract

Spiking neural networks (SNNs) and neuromorphic systems have attracted ever increasing interests recently, due to their high computational and energy efficiencies originated from closely imitating the functional mechanism of cerebral cortex, which adopts sparse spikes for information processing. In this work, we present a low-cost real-time face recognition system for potential edge-side intelligent applications. This system is mainly built upon our prior reported MorphBungee neuromorphic chip, which is capable of fast on-chip deep learning for fully-connected (FC) SNN of up to 4 layers, 1K spiking neurons and 256K synapses, under a low power consumption of about 100 mW. Our face recognition system achieves 20-fps and 30-fps image frame rates for real-life human face learning and inference, respectively, and obtains a high face recognition accuracy of 100% among 6 persons. It demonstrates that our face recognition system with the neuromorphic chip is suitable for resource-limited real-time intelligent edge applications.