Implementing Convolutional Neural Networks on FPGA: A Survey and Research

¹ LabTIC, ENSA, Abdelmalek Essaadi University, Tangier, Morocco
² TIM Team, ENSA, Cadi Ayyad University, Marrakech, Morocco
³ Universidad Politécnica de Cartagena, Cartagena, Spain

^* Corresponding author: Haijoub.Abdel@gmail.com

Abstract

The implementation of CNN FPGA is of increasing importance due to the growing demand for low-power and high-performance edge AI applications. This paper presents a comprehensive survey and research on the topic, with a focus on comparing and evaluating the performance of two main FPGA architectures, streaming and single unit computing. The study includes a detailed evaluation of the state-of-the-art CNNs, LeNet-5 and YOLOv2, on both FPGA architectures. The results provide useful insights into the trade-offs involved, limitations, challenges, and the complexity of implementing CNNs on FPGAs. The paper highlights the difficulties and intricacies involved in implementing CNNs on FPGAs and provides potential solutions for improving performance and efficiency.