Neuromorphic Computing and Spiking Neural Networks for Industrial Intelligence: Energy-Efficient Edge AI, Real-Time Sensory Processing, and the Neuromorphic Smart Factory

Abstract

The proliferation of artificial intelligence in manufacturing — from process optimization and quality inspection to predictive maintenance and collaborative robotics — confronts a fundamental physical bottleneck: the energy consumption and inference latency of conventional GPU- and TPU-based AI accelerators cannot meet the demands of real-time, at-the-edge AI processing across thousands of sensors, machines, and robots in modern factories. Neuromorphic computing — computing architectures inspired by the structure and function of biological neural systems — and spiking neural networks (SNNs) — the third generation of neural networks that encode information in the timing of discrete spikes — have emerged as a transformative alternative, offering event-driven, massively parallel, and energy-efficient computation that is natively suited to the temporal dynamics of industrial sensor data. This review provides a comprehensive synthesis of neuromorphic computing and SNNs for industrial intelligence, examining the neuromorphic sensing-computation co-design paradigm, SNNs for real-time industrial quality inspection, neuromorphic edge AI for smart manufacturing, the integration of neuromorphic systems with industrial IoT platforms, and hybrid neuromorphic-classical architectures for complex manufacturing analytics. We further connect these advances to industrial optical sensing technologies — precision 3D surface metrology and four-dimensional thermal imaging — demonstrating how neuromorphic sensing-computation co-design enables ultra-low-latency, energy-efficient quality intelligence at the factory edge. A central contribution is the articulation of an integrated Neuromorphic Industrial Intelligence Architecture (NIIA) that unifies neuromorphic sensing, spiking neural network inference, and hybrid classical-neuromorphic computing for the next generation of energy-efficient, real-time smart manufacturing.