Implementation of deep learning models in FPGA development board for recognition accuracy enhancement

Abstract

Deep learning (DL) model performance is intricately tied to the quality of training, influenced by several parameters. Of these, the computing unit employed significantly impacts training efficiency. Traditional setups use central processing units (CPUs) or graphics processing units (GPUs) for DL training. This paper proposes an alternative using field programmable gate arrays (FPGAs) for DL training, leveraging their customizable and parallelizable architecture. FPGA programming allows for tailored circuit designs, optimizing DL training requirements and enabling efficient parallel processing. The use of FPGAs in DL training has garnered attention for their potential in achieving high computational throughput and energy efficiency, attributed to advantages like low latency, high bandwidth, and reconfigurability. By exploiting FPGA parallel processing capabilities, faster training times and the potential for larger, more complex DL models are feasible. This paper provides an overview of state-of-the-art techniques for FPGA-based DL model training, discussing challenges such as hardware architecture design, memory management, and algorithm optimization. Additionally, various FPGA-based DL frameworks and libraries facilitating DL model development and deployment on FPGAs are explored.