Journal of Research in Electrical Circuits and Systems ISSN: 3139-2059 (Online)

Authors: Dr. Kiran R. Deshpande, Meera S. Rao

Abstract: The increasing complexity and computational demands of artificial intelligence (AI) algorithms have necessitated the development of specialized hardware systems. Hardware realization of AI enables faster processing, lower latency, reduced energy consumption, and enhanced integration of machine learning models in edge devices. This paper explores key hardware platforms for AI realization, including GPUs, FPGAs, ASICs, and emerging neuromorphic architectures. We discuss circuit-level implementations of AI primitives, such as matrix multiplication, activation functions, and convolutional layers, highlighting analog and digital approaches. The paper also presents Indian research contributions, design challenges, system architectures, and applications in robotics, autonomous vehicles, and IoT systems. Tables, 2D figures, and references illustrate the state of the art and practical considerations for AI hardware realization.

Keywords: Artificial intelligence hardware, FPGA, ASIC, Neuromorphic circuits, AI accelerators, Edge computing