Mapping code on Coarse Grained Reconfigurable Arrays using a SAT solver

TL;DR

This paper presents a SAT-based approach to optimize code mapping on Coarse-Grain Reconfigurable Arrays, improving compilation time and mapping quality for accelerating compute-intensive workloads.

Contribution

It introduces a novel SAT formulation and Kernel Mobility Schedule for better mapping and II minimization on CGRAs, surpassing existing techniques.

Findings

Reduces compilation time compared to state-of-the-art methods.

Achieves higher quality mappings with lower iteration intervals.

Demonstrates effectiveness on various CGRA topologies.

Abstract

Emerging low-powered architectures like Coarse-Grain Reconfigurable Arrays (CGRAs) are becoming more common. Often included as co-processors, they are used to accelerate compute-intensive workloads like loops. The speedup obtained is defined by the hardware design of the accelerator and by the quality of the compilation. State of the art (SoA) compilation techniques leverage modulo scheduling to minimize the Iteration Interval (II), exploit the architecture parallelism and, consequentially, reduce the execution time of the accelerated workload. In our work, we focus on improving the compilation process by finding the lowest II for any given topology, through a satisfiability (SAT) formulation of the mapping problem. We introduce a novel schedule, called Kernel Mobility Schedule, to encode all the possible mappings for a given Data Flow Graph (DFG) and for a given II. The schedule is used together with the CGRA architectural information to generate all the constraints necessary to find a valid mapping. Experimental results demonstrate that our method not only reduces compilation time on average but also achieves higher quality mappings compared to existing SoA techniques.