BENCHMARKING AND IMPLEMENTING DEEP LEARNING ALGORITHMS ON FIELD PROGRAMMABLE GATE ARRAYS AND APLLICATION SPECIFIC INTEGRAL CIRCUIT PLATFORMS

Abstract

Issues on the enhancement of Artificial Intelligence (AI) performance using Field-Programmable Gate Arrays (FPGA) and Application-Specific Integrated Circuits (ASIC) are studied. It focuses on benchmarking and implementing deep learning algorithms, crucial components of modern AI, on these advanced hardware platforms. The study begins with an explanation of the significance of deep learning in AI and the growing need for efficient computing platforms like FPGA and ASIC. These platforms are known for their high-speed processing capabilities and low power consumption, making them ideal for AI applications. The research then delves into a detailed analysis of how deep learning algorithms can be optimized and executed on FPGA and ASIC platforms. It highlights the methods used to benchmark the performance of these algorithms on the mentioned hardware, providing a clear comparison with traditional computing systems. The paper also discusses the challenges and solutions in integrating deep learning algorithms into these specialized hardware environments. Further, the advantages of using FPGA and ASIC for AI tasks, including improved processing speed, reduced energy consumption, and enhanced ability to handle complex AI computations are studied.