A Framework for Automated CGRA Design Space Exploration with Genetic Algorithm Optimization

The rapid growth of compute-intensive applications has created a pressing need for computing architectures that effectively balance flexibility, efficiency, and performance. While Field-Programmable Gate Arrays (FPGAs) offer a good level of flexibility, they suffer from high configuration overhead and energy consumption. Coarse-Grained Reconfigurable Architectures (CGRAs) provide a more energy-efficient alternative with lower configuration costs. They can be customized for domainspecific applications by modifying their coarse-grained processing elements to execute particular sequences of operations. In fact, their domain-specific nature can be used to further improve their energy efficiency and reduce their area overhead by exploiting computing fabric specialization. This can be achieved by replacing homogeneous processing elements with a subset of heterogeneous, more optimized ones that are specifically suited to the target application domain. However, achieving an optimal CGRA configuration requires extensive design space exploration (DSE), which involves evaluating many architectural possibilities. Existing CGRA frameworks struggle with slow and inefficient exploration due to long runtimes and constrained customization options. These issues make it hard to find the best configurations rapidly. To tackle these challenges, this paper presents Genetic Algorithm-based CGRA Generator (GA-CG), a framework that enhances DSE in the CGRA design process. GA-CG uses a genetic algorithm to discover an efficient structural configuration, thereby improving resource utilization and reducing power consumption.