FPGA-based Architecture for Image Processing using Convolutional Neural Networks

Чумак, В. С.; Tsivinskyi, V.

Публікація:
FPGA-based Architecture for Image Processing using Convolutional Neural Networks

dc.contributor.author	Чумак, В. С.
dc.contributor.author	Tsivinskyi, V.
dc.date.accessioned	2023-12-19T20:42:20Z
dc.date.available	2023-12-19T20:42:20Z
dc.date.issued	2023
dc.description.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.
dc.identifier.citation	Chumak, V. FPGA-based Architecture for Image Processing using Convolutional Neural Networks / V. Chumak, V. Tsivinskyi // V International Scientific and Practical Conference Theoretical and Applied Aspects of Device Development on Microcontrollers and FPGAs (MC&FPGA-2023), Kharkiv, Ukraine, 2023, pp. 44-46.
dc.identifier.uri	https://openarchive.nure.ua/handle/document/25104
dc.language.iso	en
dc.publisher	MC&FPGA
dc.subject	Applications
dc.subject	architecture
dc.subject	CNN
dc.subject	FPGA
dc.subject	hardware
dc.subject	image processing
dc.subject	interfaces
dc.subject	software
dc.title	FPGA-based Architecture for Image Processing using Convolutional Neural Networks
dc.type	Thesis
dspace.entity.type	Publication