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- W4308659767 abstract "Detailed Placement (DP) is an important, but time-consuming, optimization step within the Field Programmable Gate Array (FPGA) design flow. Given a global placement, DP seeks to refine the global placement to improve the success of the subsequent routing step. In this paper, we show how Reinforcement Learning (RL) can be used to significantly reduce DP runtimes while maintaining Quality-of-Result (QoR). We develop 3 different RL models based on Tabular Q-Learning, Deep Q-Learning, and Actor-Critic. These models are evaluated by integrating them into GPlace3.0 – a state-of-the-art analytic FPGA placement tool – and tested using the 12 ISPD contest benchmarks. Our results show the models achieve total runtime improvements between 2x to 3.5x and similar QoR compared to GPlace3.0’s algorithmic-based detailed placer." @default.
- W4308659767 created "2022-11-13" @default.
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- W4308659767 date "2022-10-03" @default.
- W4308659767 modified "2023-10-14" @default.
- W4308659767 title "Guiding FPGA Detailed Placement via Reinforcement Learning" @default.
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- W4308659767 doi "https://doi.org/10.1109/vlsi-soc54400.2022.9939580" @default.
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