Accelerating embedded image processing for real time: a case study
Many image processing applications need real-time performance, while having restrictions of size, weight and power consumption. Common solutions, including hardware/software co-designs, are based on Field Programmable Gate Arrays (FPGAs). Their main drawback is long development time. In this work, a...
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paper:paper_18618200_v_n_p1_Pedre2023-06-08T16:29:31Z Accelerating embedded image processing for real time: a case study Pedre, Sol Hardware acceleration High level modeling High level synthesis Methodology for hardware/software co-design in FPGA Multiple robot localization Multithreaded programming Real-time image processing Many image processing applications need real-time performance, while having restrictions of size, weight and power consumption. Common solutions, including hardware/software co-designs, are based on Field Programmable Gate Arrays (FPGAs). Their main drawback is long development time. In this work, a co-design methodology for processor-centric embedded systems with hardware acceleration using FPGAs is proposed. The goal of this methodology is to achieve real-time embedded solutions, using hardware acceleration, but achieving development time similar to that of software projects. Well established methodologies, techniques and languages from the software domain-such as Object-Oriented Paradigm design, Unified Modelling Language, and multithreading programming-are applied; and semiautomatic C-to-HDL translation tools and methods are used and compared. The methodology is applied to achieve an embedded implementation of a global vision algorithm for the localization of multiple robots in an e-learning robotic laboratory. The algorithm is specifically developed to work reliably 24/7 and to detect the robot's positions and headings even in the presence of partial occlusions and varying lighting conditions expectable in a normal classroom. The co-designed implementation of this algorithm processes 1,600 × 1,200 pixel images at a rate of 32 fps with an estimated energy consumption of 17 mJ per frame. It achieves a 16× acceleration and 92 % energy saving, which compares favorably with the most optimized embedded software solutions. This case study shows the usefulness of the proposed methodology for embedded real-time image processing applications. © 2013 Springer-Verlag Berlin Heidelberg. Fil:Pedre, S. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. 2013 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_18618200_v_n_p1_Pedre http://hdl.handle.net/20.500.12110/paper_18618200_v_n_p1_Pedre |
institution |
Universidad de Buenos Aires |
institution_str |
I-28 |
repository_str |
R-134 |
collection |
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
topic |
Hardware acceleration High level modeling High level synthesis Methodology for hardware/software co-design in FPGA Multiple robot localization Multithreaded programming Real-time image processing |
spellingShingle |
Hardware acceleration High level modeling High level synthesis Methodology for hardware/software co-design in FPGA Multiple robot localization Multithreaded programming Real-time image processing Pedre, Sol Accelerating embedded image processing for real time: a case study |
topic_facet |
Hardware acceleration High level modeling High level synthesis Methodology for hardware/software co-design in FPGA Multiple robot localization Multithreaded programming Real-time image processing |
description |
Many image processing applications need real-time performance, while having restrictions of size, weight and power consumption. Common solutions, including hardware/software co-designs, are based on Field Programmable Gate Arrays (FPGAs). Their main drawback is long development time. In this work, a co-design methodology for processor-centric embedded systems with hardware acceleration using FPGAs is proposed. The goal of this methodology is to achieve real-time embedded solutions, using hardware acceleration, but achieving development time similar to that of software projects. Well established methodologies, techniques and languages from the software domain-such as Object-Oriented Paradigm design, Unified Modelling Language, and multithreading programming-are applied; and semiautomatic C-to-HDL translation tools and methods are used and compared. The methodology is applied to achieve an embedded implementation of a global vision algorithm for the localization of multiple robots in an e-learning robotic laboratory. The algorithm is specifically developed to work reliably 24/7 and to detect the robot's positions and headings even in the presence of partial occlusions and varying lighting conditions expectable in a normal classroom. The co-designed implementation of this algorithm processes 1,600 × 1,200 pixel images at a rate of 32 fps with an estimated energy consumption of 17 mJ per frame. It achieves a 16× acceleration and 92 % energy saving, which compares favorably with the most optimized embedded software solutions. This case study shows the usefulness of the proposed methodology for embedded real-time image processing applications. © 2013 Springer-Verlag Berlin Heidelberg. |
author |
Pedre, Sol |
author_facet |
Pedre, Sol |
author_sort |
Pedre, Sol |
title |
Accelerating embedded image processing for real time: a case study |
title_short |
Accelerating embedded image processing for real time: a case study |
title_full |
Accelerating embedded image processing for real time: a case study |
title_fullStr |
Accelerating embedded image processing for real time: a case study |
title_full_unstemmed |
Accelerating embedded image processing for real time: a case study |
title_sort |
accelerating embedded image processing for real time: a case study |
publishDate |
2013 |
url |
https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_18618200_v_n_p1_Pedre http://hdl.handle.net/20.500.12110/paper_18618200_v_n_p1_Pedre |
work_keys_str_mv |
AT pedresol acceleratingembeddedimageprocessingforrealtimeacasestudy |
_version_ |
1768546180004839424 |