Software transparent dynamic binary translation for coarse-grain reconfigurable architectures

Abstract

The end of Dennard Scaling has forced architects to focus on designing for execution efficiency. Course-grained reconfigurable architectures (CGRAs) are a class of architectures that provide a configurable grouping of functional units that aim to bridge the gap between the power and performance of custom hardware and the flexibility of software. Despite their potential benefit, CGRAs face a major adoption challenge as they do not execute a standard instruction stream. Dynamic translation for CGRAs has the potential to solve this problem, but faces non-trivial challenges. Existing attempts either do not achieve the full power and performance potential CGRAs offer or suffer from excessive translation time. In this work we propose DORA, a Dynamic Optimizer for Reconfigurable Architectures, which achieves substantial (2X) power and performance improvements while having low hardware and insertion overhead and benefiting the current execution. In addition to traditional optimizations, DORA leverages dynamic register information to perform optimizations not available to compilers and achieves performance similar to or better than CGRA-targeted compiled code.