Advanced Neuromorphic Chip Design for Energy-Efficient Artificial Intelligence Using Spiking Neural Networks and Memristive Synapses

Abstract

The exponential growth of Artificial Intelligence (AI) applications has created unprecedented demands for computational power, energy efficiency, and real-time data processing. Conventional von Neumann computing architectures suffer from significant limitations, including high power consumption, memory bottlenecks, and inefficient execution of brain-inspired algorithms. Neuromorphic computing has emerged as a promising paradigm that mimics the structure and functionality of biological neural systems to achieve highly efficient information processing. Neuromorphic chips integrate neuron-inspired processing elements, synaptic networks, event-driven communication mechanisms, and adaptive learning capabilities into specialized hardware platforms. This paper presents an advanced neuromorphic chip design framework that combines spiking neural networks, memristive synapses, asynchronous processing, and low-power VLSI techniques to develop an energy-efficient intelligent computing architecture. The proposed system aims to emulate biological neural behavior while reducing computational complexity and power consumption. Through event-driven processing and distributed memory computation, the architecture achieves superior performance for machine learning, edge AI, robotics, and cognitive computing applications. Experimental evaluation demonstrates significant improvements in energy efficiency, processing latency, and scalability compared to conventional AI hardware architectures. The proposed framework highlights the potential of neuromorphic engineering in shaping future intelligent systems capable of adaptive and autonomous learning. Keywords— Neuromorphic Computing, Neuromorphic Chips, Spiking Neural Networks, Memristors, Artificial Intelligence Hardware, VLSI Design, Brain-Inspired Computing, Edge AI.