SmartMap: Architecture-Agnostic CGRA Mapping Using Graph Traversal and Reinforcement Learning

Coarse-Grained Reconfigurable Architectures (CGRAs) have been the subject of extensive research due to their balance between performance, energy efficiency, and flexibility. CGRAs must be capable of executing a dataflow graph (DFG), which depends on a compiler producing quality valid mappings with feasible running time performance and portable mapping DFGs on different CGRA architectures. Machine learning-based compilers have shown promising results by presenting high quality and performance but offer limited portability. Moreover, some approaches do not explore efficient placement methods or do not demonstrate whether scaling to more challenging, less connected architectures. This paper presents SmartMap, an architecture-agnostic framework that uses an actor-critic reinforcement learning method applied to a Monte-Carlo Tree Search (MCTS) to learn how to map a DFG onto a CGRA. This framework offers full portability using a state-action representation layer in the policy network instead of a probability distribution over actions. SmartMap uses a graph traversal placement method to provide scalability and improve the efficiency of MCTS by enabling more efficient exploration during the search. Our results show that SmartMap has 2.81x more mapping capacity, a 16.82x speed-up in compilation time, and consumes fewer resources compared to the state-of-the-art.