Polyhedral-based Pipelining of Imperfectly-Nested Loop for CGRAs
Dajiang Liu, Ting Liu, Xingyu Mo, Jiaxing Shang, Shouyi Yin · 2021
Coarse-Grained Reconfigurable Architectures (CGRAs) are promising architectures with high energy efficiency and flexibility. The computation-intensive portions of an application (e.g. loops) are often executed on CGRAs for acceleration and modulo scheduling is commonly used for loop mapping. However, for imperfectly-nested loops, existing methods don't fully explore the structure of the loops before performing modulo scheduling, resulting in poor execution performance. To tackle this problem, we propose a polyhedral-based pipelining approach for mapping imperfectly-nested loops on CGRA. By efficiently exploring the transformation space for imperfectly-nested loops using the polyhedral model and taking total execution time as an optimization metric, our approach could improve the execution performance greatly. On a$4\times 4$mesh-connected CGRA, the experimental results show that our approach can reduce the total execution time of nested loop by 50.1 % on average, as compared to the state-of-the-art techniques. Moreover, the compilation time is moderate in practice.