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- W4380881133 abstract "Coarse-grained reconfigurable architecture (CGRA) has become a promising candidate for data-intensive computing due to its flexibility and high energy efficiency. CGRA compilers map data flow graphs (DFGs) extracted from applications onto CGRAs, playing a fundamental role in fully exploiting hardware resources for acceleration. Yet the existing compilers are time-demanding and cannot guarantee optimal results due to the traversal search of enormous search spaces brought about by the spatio-temporal flexibility of CGRA structures and the complexity of DFGs. Inspired by the amazing progress in reinforcement learning (RL) and Monte-Carlo tree search (MCTS) for real-world problems, we consider constructing a compiler that can learn from past experiences and comprehensively understand the target DFG and CGRA." @default.
- W4380881133 created "2023-06-17" @default.
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- W4380881133 date "2023-06-17" @default.
- W4380881133 modified "2023-10-14" @default.
- W4380881133 title "MapZero: Mapping for Coarse-grained Reconfigurable Architectures with Reinforcement Learning and Monte-Carlo Tree Search" @default.
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- W4380881133 doi "https://doi.org/10.1145/3579371.3589081" @default.
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