A Survey on Coarse-Grained Reconfigurable Architectures From a Performance Perspective
With the end of both Dennard's scaling and Moore's law, computer users and researchers are aggressively exploring alternative forms of computing in order to continue the performance scaling that we have come to enjoy. Among the more salient and practical of the post-Moore alternatives are...
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doaj-b2f484461f274df48ae1a1ad21ef16192021-03-30T04:13:24ZengIEEEIEEE Access2169-35362020-01-01814671914674310.1109/ACCESS.2020.30120849149601A Survey on Coarse-Grained Reconfigurable Architectures From a Performance PerspectiveArtur Podobas0https://orcid.org/0000-0001-5452-6794Kentaro Sano1Satoshi Matsuoka2RIKEN Center for Computational Science, Kobe, JapanRIKEN Center for Computational Science, Kobe, JapanRIKEN Center for Computational Science, Kobe, JapanWith the end of both Dennard's scaling and Moore's law, computer users and researchers are aggressively exploring alternative forms of computing in order to continue the performance scaling that we have come to enjoy. Among the more salient and practical of the post-Moore alternatives are reconfigurable systems, with Coarse-Grained Reconfigurable Architectures (CGRAs) seemingly capable of striking a balance between performance and programmability. In this paper, we survey the landscape of CGRAs. We summarize nearly three decades of literature on the subject, with a particular focus on the premise behind the different CGRAs and how they have evolved. Next, we compile metrics of available CGRAs and analyze their performance properties in order to understand and discover knowledge gaps and opportunities for future CGRA research specialized towards High-Performance Computing (HPC). We find that there are ample opportunities for future research on CGRAs, in particular with respect to size, functionality, support for parallel programming models, and to evaluate more complex applications.https://ieeexplore.ieee.org/document/9149601/Coarse-grained reconfigurable architecturesCGRAFPGAcomputing trendsreconfigurable systemshigh-performance computing |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Artur Podobas Kentaro Sano Satoshi Matsuoka |
spellingShingle |
Artur Podobas Kentaro Sano Satoshi Matsuoka A Survey on Coarse-Grained Reconfigurable Architectures From a Performance Perspective IEEE Access Coarse-grained reconfigurable architectures CGRA FPGA computing trends reconfigurable systems high-performance computing |
author_facet |
Artur Podobas Kentaro Sano Satoshi Matsuoka |
author_sort |
Artur Podobas |
title |
A Survey on Coarse-Grained Reconfigurable Architectures From a Performance Perspective |
title_short |
A Survey on Coarse-Grained Reconfigurable Architectures From a Performance Perspective |
title_full |
A Survey on Coarse-Grained Reconfigurable Architectures From a Performance Perspective |
title_fullStr |
A Survey on Coarse-Grained Reconfigurable Architectures From a Performance Perspective |
title_full_unstemmed |
A Survey on Coarse-Grained Reconfigurable Architectures From a Performance Perspective |
title_sort |
survey on coarse-grained reconfigurable architectures from a performance perspective |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
With the end of both Dennard's scaling and Moore's law, computer users and researchers are aggressively exploring alternative forms of computing in order to continue the performance scaling that we have come to enjoy. Among the more salient and practical of the post-Moore alternatives are reconfigurable systems, with Coarse-Grained Reconfigurable Architectures (CGRAs) seemingly capable of striking a balance between performance and programmability. In this paper, we survey the landscape of CGRAs. We summarize nearly three decades of literature on the subject, with a particular focus on the premise behind the different CGRAs and how they have evolved. Next, we compile metrics of available CGRAs and analyze their performance properties in order to understand and discover knowledge gaps and opportunities for future CGRA research specialized towards High-Performance Computing (HPC). We find that there are ample opportunities for future research on CGRAs, in particular with respect to size, functionality, support for parallel programming models, and to evaluate more complex applications. |
topic |
Coarse-grained reconfigurable architectures CGRA FPGA computing trends reconfigurable systems high-performance computing |
url |
https://ieeexplore.ieee.org/document/9149601/ |
work_keys_str_mv |
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