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- W4386065660 abstract "Logic optimization involves a set of Boolean transformations, and the different ways of permutation of Boolean transformations affect the circuit performance. The design space composed of these Boolean transformations is exponential. However, heuristic algorithms often need the guidance of human experience, and the result is not ideal for this large-scale search space problem. In this paper, the problem of logic synthesis optimization is mapped to the sequential decision process. We introduce policy search based reinforcement learning, which makes optimization decisions by analyzing data flow generated by synthesis tools, and find the optimal solution for logic synthesis. The evaluation of EPFL benchmark circuit shows that compared with the original design, the nodes are reduced by 19.60%, the depth is reduced by 13.76%. Compared with ABC script resyn2, the number of nodes is reduced by 6.70%, the depth is reduced by 7.76%. In addition, the performance of the scheme is successfully verified by the sequential circuit." @default.
- W4386065660 created "2023-08-23" @default.
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- W4386065660 date "2023-06-26" @default.
- W4386065660 modified "2023-09-27" @default.
- W4386065660 title "Logic Optimization Sequence Tuning Based on Policy Search Deep Reinforcement Learning" @default.
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- W4386065660 doi "https://doi.org/10.1109/cstic58779.2023.10219326" @default.
- W4386065660 hasPublicationYear "2023" @default.
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