Raccoon: Lightweight Support for Comprehensive Control Flows in Reconfigurable Spatial Architectures

Coarse-grained reconfigurable arrays (CGRAs) have emerged as promising candidates for digital signal processing, biomedical, and automotive applications, where energy efficiency and flexibility are paramount. Yet existing CGRAs suffer from the Amdahl bottleneck caused by constrained control handling via either off-device communication or expensive tag-matching mechanisms. More importantly, mapping control flow onto CGRAs is extremely arduous and time-consuming due to intricate instruction structures and hardware mechanisms. To counteract these limitations, we propose Raccoon, a portable and lightweight framework for CGRAs targeting vast control flows. Raccoon comprises a comprehensive approach that spans microarchitecture, HW/SW interface, and compiler aspects. Regarding microarchitecture, Raccoon incorporates specialized infrastructure for branch- and loop-level control patterns with concise execution mechanisms. The HW/SW interface of Raccoon includes well-characterized abstractions and instruction sets tailored for easy compilation, featuring custom operators and architectural models for control-oriented units. On the compiler front, Raccoon integrates advanced control handling techniques and employs a portable mapper leveraging reinforcement learning and Monte Carlo tree search. This enables agile mapping and optimization of the entire program, ensuring efficient execution and high-quality results. Through the cohesive co-design, Raccoon can empower various CGRAs with robust control-flow handling capabilities, surpassing conventional tagged mechanisms in terms of hardware efficiency and compiler adaptability. Evaluation results show that Raccoon achieves up to a 5.78× improvement in energy efficiency and a 2.24× reduction in cycle count over state-of-the-art CGRAs. Raccoon stands out for its versatility in managing intricate control flows and showcases remarkable portability across diverse CGRA architectures.