Designing of Neuromorphic VLSI Circuits based on Biological Neural Networks to Improve the Energy Efficiency and Performance of AI/ML Applications

Abstract

Neuromorphic VLSI circuits, inspired by biological neural networks, have emerged as a promising solution to address the growing demand for energy-efficient and high-performance AI/ML applications. Traditional computing architectures face limitations in power consumption, scalability, and real-time processing, especially for complex, data-intensive tasks. In this research, we propose the design and implementation of neuromorphic VLSI circuits that mimic the structure and functionality of biological neurons and synapses. By leveraging event-driven, asynchronous spiking neural networks (SNNs), our circuits are able to process information in a parallel and distributed manner, significantly reducing power consumption while improving computation speed. The proposed neuromorphic circuits integrate in-memory computing, which eliminates the energy bottlenecks associated with data transfer between memory and processing units in conventional systems. This paper highlights the architectural advancements in VLSI design that enable real-time learning and adaptation, making these circuits highly suited for AI/ML tasks such as image recognition, natural language processing, and autonomous systems. Simulation results demonstrate that our neuromorphic VLSI circuits achieve superior energy efficiency and performance compared to traditional AI hardware. This research opens new avenues for developing low-power, scalable AI solutions in edge computing and other energy-constrained environments.