NEUROMORPHIC COMPUTING AND BRAIN-INSPIRED ARCHITECTURES

Abstract

Neuromorphic computing and brain-inspired architectures constitute a revolutionary paradigm for designing hardware and algorithms inspired by human neural behavior. In contrast to traditional von Neumann systems, neuromorphic architectures couple memory and computation together in a highly parallel, event-driven fashion, which allows orders- of-magnitude reductions in energy and latency. Recent innovations—such as Intel's Loihi 2 and IBM's NorthPole— demonstrate how tightly coupled neuron-synapse circuits and spiking neural networks (SNNs) can deliver fast, adaptive learning while consuming minimal power. This paper reviews advances from the past five years in neuromorphic hardware, algorithmic paradigms (including STDP and surrogate-gradient-trained SNNs), and emerging devices like memristors. We examine technical specifics of architectural designs, learning regulations, and benchmarks demonstrating 10–1000× energy benefits over GPUs for AI applications like keyword spotting and scientific simulations. The paper also mentions applications in edge AI, robotics, and biomedical implants and lists current challenges in software programmability, algorithmic maturity, and hardware scaling. We conclude that neuromorphic computing presents a realistic route to sustainable, real-time AI for edge devices and domain-specific data-center workloads, with the potential to revolutionize the future of computing with brain-inspired design principles