Neuromorphic Computing: Current Progress and the Future of Brain-Inspired Computing

Abstract

Neuromorphic computing borrows its design logic from the nervous system rather than from the von Neumann architecture that has dominated computing for seventy years. Instead of shuttling data back and forth between separate memory and processing units, it favors event-driven communication, computation that happens close to (or inside) memory, and massive parallelism across simple processing elements. This paper takes stock of where the field currently stands: spiking neural networks, digital and analog processors, memristive and other emerging devices, the software ecosystems that support them, and application areas ranging from robotics and edge intelligence to biomedical monitoring and event-based vision. What emerges from the recent literature is a field that has largely moved past small proof-of-concept chips and is now building larger, more programmable platforms with tighter hardware-algorithm integration. Even so, real obstacles remain around training methods, benchmarking practices, programmability, device variability, and fabrication, and it is still unclear how much of the field's energy advantage survives contact with general-purpose workloads. The view taken here is that brain-inspired computing is heading toward a hybrid future: conventional digital processors will keep doing what they do best, while event-driven and in-memory accelerators take over the workloads where they have a genuine edge. Neuromorphic computing is therefore unlikely to displace mainstream AI hardware outright, but it looks well positioned to become a key ingredient in low-power, adaptive, real-time intelligence at the edge. Keywords: edge AI; neuromorphic computing; brain-inspired computing; spiking neural networks; memristors; in-memory computing