Developing FPGA-Based Accelerators for Deep Learning in Reconfigurable Computing Systems

Abstract

The incorporation of Field-Programmable Gate Arrays (FPGAs) into deep learning frameworks has paved the way for significant improvements in computational performance and energy efficiency within reconfigurable computing systems. This study investigates the creation and deployment of FPGA-based accelerators designed specifically for deep learning tasks. It begins with a comprehensive overview of the architectural design principles and hardware aspects pertinent to FPGA accelerators. The analysis then shifts to performance metrics, evaluating FPGA accelerators against conventional GPU and CPU systems in terms of speed, efficiency, and scalability. Furthermore, the paper explores various optimization strategies aimed at enhancing energy efficiency and throughput in FPGA implementations. Practical applications and advantages of FPGA accelerators are highlighted through case studies in real-world deep learning contexts. The study concludes with a discussion on future trends and challenges, underlining the potential of FPGAs to foster innovation in deep learning and reconfigurable computing. This research underscores the pivotal role of FPGAs in elevating the capabilities of deep learning systems, providing detailed insights into their development and optimization.