FPGA-Based Real-Time Deep Learning Application in PYNQ: A Review Toward Agricultural Prospects

Field Programmable Gate Arrays (FPGAs) have emerged as powerful hardware accelerators for deep learning applications due to their reconfigurable nature and parallel processing capabilities. Among FPGA development platforms, Python Productivity for Zynq (PYNQ) stands out as a notable option, providing a Python interface to program Xilinx Zynq FPGA SoCs. This paper presents a comprehensive review of research papers focusing on FPGA-based real-time deep learning applications developed within the PYNQ framework. It delves into the advantages of utilizing FPGAs for deep learning, the seamless integration of deep learning models into PYNQ platforms, and a detailed analysis of the performance enhancements achieved through FPGA acceleration. Furthermore, challenges and opportunities in the agriculture domain have been discussed for future research in this evolving domain. Deep learning in agriculture, leveraging FPGA and PYNQ, may revolutionize the development of real-time applications.