A Survey on Coarse-Grained Reconfigurable Architectures From a Performance Perspective
A Survey on Coarse-Grained Reconfigurable Architectures From a Performance Perspective
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DOI：
10.1109/access.2020.3012084
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发表时间：
2020-04
期刊：
影响因子：
3.9
通讯作者：
Artur Podobas;K. Sano;S. Matsuoka
中科院分区：
文献类型：
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作者：
Artur Podobas;K. Sano;S. Matsuoka
随着Dennard缩放和摩尔定律的终结，计算机用户和研究人员正在积极探索计算的替代形式，以继续我们所享受的性能缩放。在后摩尔时代的替代方案中，更突出和实用的是可重构系统，粗粒度可重构架构（CGRA）似乎能够在性能和可编程性之间取得平衡。在本文中，我们调查的CGRAs的景观。我们总结了近三十年来关于这一主题的文献，特别关注不同CGRA背后的前提以及它们是如何演变的。接下来，我们编译可用CGRA的指标，并分析其性能属性，以了解和发现未来专门针对高性能计算（HPC）的CGRA研究的知识差距和机会。我们发现，有足够的机会，未来的研究CGRAs，特别是在规模，功能，支持并行编程模型，并评估更复杂的应用程序。
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.