A Survey on Coarse-Grained Reconfigurable Architectures From a Performance Perspective

TLDR

With the end of Dennard scaling and Moore’s law, researchers are turning to alternative computing paradigms, and coarse‑grained reconfigurable architectures (CGRAs) offer a promising balance between performance and programmability. This paper surveys the CGRA landscape. The authors compile performance metrics from nearly three decades of literature, analyze them to identify gaps, and highlight opportunities for future CGRA research in high‑performance computing. They conclude that future CGRA work should focus on scaling size, expanding functionality, supporting parallel programming models, and evaluating more complex applications.

Abstract

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.