Optimizing Imperfectly-Nested Loop Mapping on CGRAs via Polyhedral-Guided Flattening.

Coarse-Grained Reconfigurable Arrays (CGRAs) offer a promising balance between high performance and power efficiency. To reduce the invocation overhead when mapping an imperfectly nested loop, loop flattening is used to transform the nested loop into a single-level loop. However, loop flattening not only leads to a big loop body but also has a narrow application scope. To this end, this work proposes a polyhedral model-based loop flattening approach for imperfectly nested loop mapping. By exploring loop structures via polyhedral transformation, we can find a flattening-friendly loop structure with more data reuse opportunities and reduced sibling loops, resulting in improved loop pipelining performance. Experimental results demonstrate a remarkable $(1.37-1.62\times)$ speedup compared to the state-of-the-art approaches while maintaining short compilation times.