A Manycore Vision Processor for Real-Time Smart Cameras

Real-time image processing and computer vision systems are now in the mainstream of technologies enabling applications for cyber-physical systems, Internet of Things, augmented reality, and Industry 4.0. These applications bring the need for Smart Cameras for local real-time processing of images and...

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Autores principales:	Bruno A. da Silva, Arthur M. Lima, Janier Arias-Garcia, Michael Huebner, Jones Yudi
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	multi-processor system-on-chip network-on-chip image processing computer vision real-time smart camera Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/5277bdb79f81422fa494a83cc5e5b8b5
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id	oai:doaj.org-article:5277bdb79f81422fa494a83cc5e5b8b5
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spelling	oai:doaj.org-article:5277bdb79f81422fa494a83cc5e5b8b52021-11-11T19:08:11ZA Manycore Vision Processor for Real-Time Smart Cameras10.3390/s212171371424-8220https://doaj.org/article/5277bdb79f81422fa494a83cc5e5b8b52021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7137https://doaj.org/toc/1424-8220Real-time image processing and computer vision systems are now in the mainstream of technologies enabling applications for cyber-physical systems, Internet of Things, augmented reality, and Industry 4.0. These applications bring the need for Smart Cameras for local real-time processing of images and videos. However, the massive amount of data to be processed within short deadlines cannot be handled by most commercial cameras. In this work, we show the design and implementation of a manycore vision processor architecture to be used in Smart Cameras. With massive parallelism exploration and application-specific characteristics, our architecture is composed of distributed processing elements and memories connected through a Network-on-Chip. The architecture was implemented as an FPGA overlay, focusing on optimized hardware utilization. The parameterized architecture was characterized by its hardware occupation, maximum operating frequency, and processing frame rate. Different configurations ranging from one to eighty-one processing elements were implemented and compared to several works from the literature. Using a System-on-Chip composed of an FPGA integrated into a general-purpose processor, we showcase the flexibility and efficiency of the hardware/software architecture. The results show that the proposed architecture successfully allies programmability and performance, being a suitable alternative for future Smart Cameras.Bruno A. da SilvaArthur M. LimaJanier Arias-GarciaMichael HuebnerJones YudiMDPI AGarticlemulti-processor system-on-chipnetwork-on-chipimage processingcomputer visionreal-timesmart cameraChemical technologyTP1-1185ENSensors, Vol 21, Iss 7137, p 7137 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	multi-processor system-on-chip network-on-chip image processing computer vision real-time smart camera Chemical technology TP1-1185
spellingShingle	multi-processor system-on-chip network-on-chip image processing computer vision real-time smart camera Chemical technology TP1-1185 Bruno A. da Silva Arthur M. Lima Janier Arias-Garcia Michael Huebner Jones Yudi A Manycore Vision Processor for Real-Time Smart Cameras
description	Real-time image processing and computer vision systems are now in the mainstream of technologies enabling applications for cyber-physical systems, Internet of Things, augmented reality, and Industry 4.0. These applications bring the need for Smart Cameras for local real-time processing of images and videos. However, the massive amount of data to be processed within short deadlines cannot be handled by most commercial cameras. In this work, we show the design and implementation of a manycore vision processor architecture to be used in Smart Cameras. With massive parallelism exploration and application-specific characteristics, our architecture is composed of distributed processing elements and memories connected through a Network-on-Chip. The architecture was implemented as an FPGA overlay, focusing on optimized hardware utilization. The parameterized architecture was characterized by its hardware occupation, maximum operating frequency, and processing frame rate. Different configurations ranging from one to eighty-one processing elements were implemented and compared to several works from the literature. Using a System-on-Chip composed of an FPGA integrated into a general-purpose processor, we showcase the flexibility and efficiency of the hardware/software architecture. The results show that the proposed architecture successfully allies programmability and performance, being a suitable alternative for future Smart Cameras.
format	article
author	Bruno A. da Silva Arthur M. Lima Janier Arias-Garcia Michael Huebner Jones Yudi
author_facet	Bruno A. da Silva Arthur M. Lima Janier Arias-Garcia Michael Huebner Jones Yudi
author_sort	Bruno A. da Silva
title	A Manycore Vision Processor for Real-Time Smart Cameras
title_short	A Manycore Vision Processor for Real-Time Smart Cameras
title_full	A Manycore Vision Processor for Real-Time Smart Cameras
title_fullStr	A Manycore Vision Processor for Real-Time Smart Cameras
title_full_unstemmed	A Manycore Vision Processor for Real-Time Smart Cameras
title_sort	manycore vision processor for real-time smart cameras
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/5277bdb79f81422fa494a83cc5e5b8b5
work_keys_str_mv	AT brunoadasilva amanycorevisionprocessorforrealtimesmartcameras AT arthurmlima amanycorevisionprocessorforrealtimesmartcameras AT janierariasgarcia amanycorevisionprocessorforrealtimesmartcameras AT michaelhuebner amanycorevisionprocessorforrealtimesmartcameras AT jonesyudi amanycorevisionprocessorforrealtimesmartcameras AT brunoadasilva manycorevisionprocessorforrealtimesmartcameras AT arthurmlima manycorevisionprocessorforrealtimesmartcameras AT janierariasgarcia manycorevisionprocessorforrealtimesmartcameras AT michaelhuebner manycorevisionprocessorforrealtimesmartcameras AT jonesyudi manycorevisionprocessorforrealtimesmartcameras
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A Manycore Vision Processor for Real-Time Smart Cameras

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