Speed Optimization of Convolutional Neural Networks (CNNs) on FPGA for Real-Time Image Classification

This research paper investigates the optimization techniques employed in implementing Convolutional Neural Networks (CNNs) on Field-Programmable Gate Arrays (FPGAs) to achieve real-time image classification. CNNs have demonstrated remarkable success in various image recognition tasks, but their deployment on resource- constrained platforms like FPGAs presents unique challenges. This paper explores methods to overcome these challenges and maximize the efficiency of CNN inference on FPGAs.