Neuromorphic Computing: Spiking Neural Networks For Edge AI

Abstract

Neuromorphic computing represents a paradigm shift in artificial intelligence by mimicking biological neural networks through spiking neural networks (SNNs). This paper explores neuromorphic computing architectures for energy-efficient AI inference at the edge. We analyze key neuromorphic hardware platforms including Intel Loihi 2, IBM TrueNorth, and BrainScaleS, examining their architectural innovations, spike-timing-dependent plasticity (STDP) learning mechanisms, and event-driven computation models. Performance evaluations demonstrate that SNNs achieve 100-1000× energy efficiency improvements compared to conventional deep neural networks for edge inference tasks. We present implementation strategies for deploying SNNs on resource-constrained edge devices, addressing challenges in spike encoding, temporal dynamics, and neuromorphic algorithm design. Our analysis reveals that neuromorphic computing offers a compelling solution for ultra-low-power AI applications in IoT, robotics, and embedded systems.