In recent years, IoT devices have become widespread, and energy-efficient coarse-grained reconfigurable architectures (CGRAs) have attracted attention. CGRAs comprise several processing units called processing elements (PEs) arranged in a two-dimensional array. The operations of PEs and the interconnections between them are adaptively changed depending on a target application, and this contributes to a higher energy efficiency compared to general-purpose processors. The application kernel executed on CGRAs is represented as a data flow graph (DFG), and CGRA compilers are responsible for mapping the DFG onto the PE array. Thus, mapping algorithms significantly influence the performance and power efficiency of CGRAs as well as the compile time. This paper proposes POCOCO, a compiler framework for CGRAs that can use pre-optimized subgraph mappings. This contributes to reducing the compiler optimization task. To leverage the subgraph mappings, we extend an existing mapping method based on a genetic algorithm. Experiments on three architectures demonstrated that the proposed method reduces the optimization time by 48%, on an average, for the best case of the three architectures.

Coarse-grained reconfigurable architectures (CGRAs) provide high energy efficiency with word-level programmability rather than bit-level ones such as FPGAs. The coarser reconfigurability brings about higher energy efficiency and reduces the complexity of compiler tasks compared to the FPGAs. However, application mapping process for CGRAs is still time-consuming. When the compiler tries to map a large and complicated application data-flow-graph(DFG) onto the reconfigurable fabric, it tends to result in inefficient resource use or to fail in mapping. In case of failure, the compiler must divide it into several sub-DFGs and goes back to the same flow. In this work, we propose a novel partitioning method based on a genetic algorithm to eliminate the unmappable DFGs and improve the mapping quality. In order not to generate unmappable sub-DFGs, we also propose an estimation model which predicts the mappability and resource requirements using a DGCNN (Deep Graph Convolutional Neural Network). The genetic algorithm with this model can seek the most resource-efficient mapping without the back-end mapping process. Our model can predict the mappability with more than 98% accuracy and resource usage with a negligible error for two studied CGRAs. Besides, the proposed partitioning method demonstrates 53-75% of memory saving, 1.28-1.39x higher throughput, and better mapping quality over three comparative approaches.