The rapid advancement of Artificial Intelligence (AI) has significantly increased the computational demands of modern electronic systems. AI applications such as deep learning, computer vision, natural language processing, autonomous vehicles, and edge intelligence require massive data processing capabilities, resulting in substantial power consumption and hardware complexity. Traditional processor architectures often struggle to meet the stringent energy efficiency requirements of AI workloads, particularly in battery-powered and resource-constrained environments. Very Large Scale Integration (VLSI) technology has emerged as a key enabler for developing specialized hardware accelerators capable of executing AI algorithms with improved performance and reduced power consumption. This paper presents a low-power VLSI architecture specifically designed for AI applications, integrating optimized processing elements, intelligent memory management, dynamic voltage scaling, and parallel computation techniques. The proposed architecture aims to minimize power dissipation while maintaining high computational throughput. Advanced architectural optimizations such as approximate computing, clock gating, power gating, and data reuse strategies are incorporated to achieve enhanced energy efficiency. Experimental analysis demonstrates significant reductions in power consumption and area utilization compared to conventional AI processing architectures. The proposed framework provides a scalable and efficient solution for implementing next-generation AI systems in edge devices, mobile platforms, and embedded intelligent applications.

Keywords— VLSI, Artificial Intelligence, Low Power Design, Hardware Accelerator, Energy Efficiency, Deep Learning, Edge Computing, AI Processor.