FPGA-based Architecture for Image Processing using Convolutional Neural Networks

Abstract

This article explores the architecture of FPGA-based Convolutional Neural Networks (CNN) for image processing. It examines the key characteristics of FPGA platforms and their impact on the performance and efficiency of CNN implementation. Special attention is given to hardware optimization, including the use of specialized blocks and algorithmic optimizations. The article also discusses interfaces and interactions with other system components, as well as software aspects for the development, debugging, and integration of FPGA-based CNNs. Examples of applications in medical imaging, automotive industry, video surveillance, and other fields are provided. This article provides an overview of the architecture and optimization of FPGA-based CNNs for image processing, highlighting their potential in various computer vision applications.