Highly scalable, shared-memory, Monte-Carlo tree search based Blokus Duo Solver on FPGA
Highly scalable, shared-memory, Monte-Carlo tree search based Blokus Duo Solver on FPGA
复制标题
FPGA 上基于高度可扩展、共享内存、蒙特卡罗树搜索的 Blokus Duo 求解器
DOI：
--
复制
发表时间：
2014
期刊：
影响因子：
--
通讯作者：
B. Alizadeh
中科院分区：
文献类型：
--
作者：
Ehsan Qasemi;Amir Samadi;Mohammad H. Shadmehr;Bardia Azizian;Sajjad Mozaffari;Amir Shirian;B. Alizadeh
在本文中，我们介绍了高度可扩展的，共享的，蒙特卡洛树搜索（MCTS）Blokus-Duo求解器的硬件体系结构。在提议的体系结构中，每个MCTS求解器模块包含一个集中的MCT控制器，该模块也可以使用具有真正的双端口访问称为Main Memore的共享内存的软核来实现，并且每个MCT引擎都包含多个模拟核心。因此，这种高度灵活的体系结构保证了求解器的优化性能，而不管使用的实际FPGA平台如何。我们的设计灵感来自平行MCTS算法，并且有可能能够从MCTS算法中获得最大可能的并行性。另一方面，在我们的设计中，我们将MCT与修剪启发式方法相结合，以增加记忆和LE利用率。结果表明，我们的体系结构可以在DE2-115平台上运行高达50MHz，每个模拟核心都需要11K LES，MCTS控制器需要10kles。
In this paper we present our hardware architecture on a highly scalable, shared-memory, Monte-Carlo Tree Search (MCTS) based Blokus-Duo solver. In the proposed architecture each MCTS solver module contains a centralized MCTS controller which can also be implemented using soft-cores with a true dual-port access to a shared memory called main memory, and multitude number of MCTS engines each containing several simulation cores. Consequently, this highly flexible architecture guaranties the optimized performance of the solver regardless of the actual FPGA platform used. Our design has been inspired from parallel MCTS algorithms and is potentially capable of obtaining maximum possible parallelism from MCTS algorithm. On the other hand, in our design we combine MCTS with pruning heuristics to increase both the memory and LE utilizations. The results show that our architecture can run up to 50MHz on DE2-115 platform, where each Simulation core requires 11K LEs and MCTS controller requires 10KLEs.