Authors

Mohammad Rizwan

Researcher, Department of Computer Science and Engineering, Lucknow Institute of Technology, Lucknow, India

Abstract

This paper explores innovative architectural frameworks that draw inspiration from the human brain's structure and functionality, with the aim of advancing artificial intelligence (AI) computing systems. As the sophistication and necessity of AI applications grow, conventional hardware architectures encounter substantial obstacles concerning power efficiency, scalability, and performance. Neuromorphic computing presents a viable approach to overcoming these challenges by incorporating the biological characteristics of neural networks. In this article, we analyze various brain-inspired mechanisms, such as firing neural networks and synaptic plasticity, to devise new systems for efficient resource sharing. These architectures seek to emulate critical brain processes, including sparse coding, event-driven processing, and real-time learning, to achieve reductions in power consumption while enhancing processing speed and flexibility. Additionally, the study investigates the incorporation of supplementary hardware elements, such as memristors and neuromorphic processors, to improve essential AI functions like pattern recognition, decision-making, and sensory analysis. The research findings are validated through simulations and hardware prototypes across six different implementations, revealing significant advancements in computational and execution performance compared to standard architectures. This work represents a valuable contribution to the ongoing evolution of next-generation AI systems, highlighting a route toward efficient and scalable AI hardware that is fundamentally based on biological neural structures, thus effectively supporting complex machine learning in real-world applications.

Keywords

Artificial intelligence Computing systems human brain AI Neuromorphic

Citation of this Article

Mohammad Rizwan. (2024). Cutting-Edge Techniques in Artificial Intelligence Processing Systems for Enhanced Dependability and Performance. Current Journal of Engineering and Science Research. 1(2), 29-38. Article DOI: https://doi.org/10.47001/CJESR/2024.102005

Licence Copyright (c) 2026 Current Journal of Engineering and Science Research. This work is licensed under a Creative Commons Attribution Non Commercial 4.0 International Licence.

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