Author:
1Prof P.T.Talole, 2Mr.Nilesh.M.Jadhav,3Mr. Ajay.S.Ingle,4Mr.Samyak.G.Sonone, 5Miss.Divyani.V.PatilPublished in
Journal of Science Technology and Research( Volume 6, Issue 1 )
Abstract
Neuromorphic computing is a transformative technology that models the human brain’s architecture to improve artificial intelligence (AI). Unlike conventional computing systems based on the von Neumann architecture, neuromorphic computing uses artificial neurons and synapses to replicate cognitive processes with high efficiency. These systems operate through massively parallel networks that process information in real time, mimicking how biological brains function.
This paper provides a comprehensive overview of neuromorphic computing. It explores the biological foundation of neural computation and details how neuromorphic hardware is designed and implemented. Special attention is given to its growing applications in machine learning, robotics, sensory processing, and intelligent systems. The architecture offers advantages such as reduced power consumption, real-time adaptability, higher memory bandwidth, and energy-efficient computation—features where traditional systems often struggle.
We also discuss critical challenges facing the field, including scalability, hardware constraints, and integration with existing technologies. By combining neuroscience and computer science, neuromorphic systems represent a major step toward building low-power, high-performance computing platforms. These systems are capable of learning, adapting, and making decisions like biological brains.
As the demand for intelligent, efficient computing systems grows, neuromorphic computing for AI will play a central role. This seminar also explores current real-world applications and forecasts the future development of neuromorphic technologies in areas like edge computing, autonomous vehicles, and neuromorphic chips. By bridging biology and engineering, this technology paves the way for the next generation of AI-driven solutions.
Introduction
Neuromorphic computing has emerged as a powerful alternative to the traditional von Neumann architecture, especially for tasks involving cognitive processing. Inspired by the human brain, this architecture uses highly interconnected artificial neurons and synapses to simulate biological processes. Unlike traditional systems, neuromorphic platforms offer improved parallelism, memory efficiency, and ultra-low power consumption.
While von Neumann systems dominate modern computing, they face limitations in power, speed, and adaptability. Neuromorphic models overcome these by co-locating memory and computation, mimicking how the brain processes information. This architecture enables advanced machine learning, real-time sensory processing, and efficient pattern recognition.