Live Demonstration: Real-time Event-based Speed Detection using Spiking Neural Networks

Arjun Roy, Manish Nagaraj, Chamika Mihiranga Liyanagedera, Kaushik Roy

Abstract: Event cameras are emerging as an ideal vision sensor for high-speed applications due to their low latency and power consumption. DOTIE, a recent work in literature, has proposed a method to detect objects through spatial and temporal isolation of events with a spiking neural network. In this work, we implement DOTIE to detect a disk moving in a circular motion and identify the speed of rotation. We further validate the claim that spiking architectures can efficiently handle events by implementing DOTIE on Intel Loihi, a neuromorphic hardware suitable for spiking neural networks, and reveal a 14× reduction in energy consumption compared to the CPU implementation of DOTIE.