Yash sharma, Vaidant Garg, Priyanka Mitra | International Journal of Microelectronics and Digital integrated circuits | Vol 12, Issue 02 | ISSN: 2456-3986
Abstract
Neuromorphic computing is an emerging computing paradigm that draws inspiration from the structure and operational principles of the human brain. By integrating ideas from neuroscience, computer design, and materials science, neuromorphic systems aim to deliver high-performance, low-power computation that is suitable for smart data processing. Unlike traditional von Neumann architectures, neuromorphic hardware uses massively parallel processing, event driven computation, and adaptive learning. This paper provides an overview of neuromorphic principles, architectures, synaptic device technologies, and learning models. The study examines core building blocks such as Spiking Neural Networks (SNNs), memristive synaptic devices, and neuromorphic hardware platforms including Intel Loihi, IBM TrueNorth, and SpiNNaker, highlighting how these components work together to emulate the efficiency of the human brain. It also reviews recent advancements and discusses potential applications in robotics, edge AI, autonomous systems, and cognitive computing while addressing challenges and future research directions. Key learning mechanisms, particularly Spike-Timing-Dependent Plasticity (STDP), are discussed as enablers of on- device, unsupervised adaptation that reduces dependence on large external datasets and continuous cloud connectivity. The paper further evaluates how in-memory computing and event-driven signalling jointly address the von Neumann bottleneck, thereby lowering latency and power consumption for always-on, real-time systems. Finally, the discussion considers the integration of neuromorphic backends with existing CMOS technology and outlines open challenges in scalability, software tooling, and standardization that must be resolved for widespread industrial adoption of brain-inspired computing architectures.
Keywords - Neuromorphic Computing, Spiking Neural Networks (SNNs), Synaptic Devices, Brain Inspired Architecture, Edge Artificial Intelligence
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How to cite this article
@article{sharmaY2026,
author = {Yash sharma and Vaidant Garg and Priyanka Mitra},
title = {Brain-Inspired Computing: The Rise of Neuromorphic AI},
journal = {International Journal of Microelectronics and Digital integrated circuits},
year = {2026},
volume = {12},
number = {02},
issn = {2456-3986},
url = {https://journalspub.com/publication/ijmdic/article=27691}
}