Design of Energy-Efficient CMOS Circuits for AI Applications

Abstract

The design of energy-efficient CMOS circuits has become a critical area of research, driven by the escalating demand for artificial intelligence (AI) applications that require immense computational power. This study investigates advanced techniques for optimizing CMOS (Complementary Metal-Oxide-Semiconductor) circuit architectures to enhance energy efficiency without compromising performance. Key areas of focus include reducing dynamic and static power consumption through innovative transistor-level designs, clock-gating strategies, and the integration of advanced materials. Additionally, the research explores the potential of hybrid architectures combining CMOS with emerging technologies like memristors and neuromorphic circuits. By addressing power constraints in AI hardware, the study aims to contribute to the development of scalable, sustainable computing systems for machine learning, neural networks, and edge AI devices. The findings have broad implications for the future of AI hardware, offering solutions that balance computational demands with energy sustainability.