PT-Map: Efficient Program Transformation Optimization for CGRA Mapping

Coarse-Grained Reconfigurable Array (CGRA) is a parallel architecture providing high energy efficiency and spatial-temporal re-configurability. Beyond loop scheduling for throughput optimization, program transformation is also crucial in CGRA mapping to optimize overall performance and efficiency. However, existing studies on program transformation optimization face challenges in exploring the transformation space systematically and evaluating candidates efficiently, leading to sub-optimal results. To tackle these challenges, this paper introduces PT-Map, an efficient program transformation optimization framework for CGRA mapping. PT-Map defines a comprehensive transformation space and employs a CGRA-specialized top-down exploration approach. It also incorporates a bottom-up evaluation scheme using architectural parameters and a graph neural network-based predictive model. Experiments demonstrate that PT-Map achieves up to 2.95X/1.80X speedups and 59.0%/23.2% energy-delay-product (EDP) reductions over the state-of-the-art approaches MapZero and PBP, respectively.