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Collaborative symmetricity exploitation for offline learning of hardware design solver
HAEYEON KIM · Minsu Kim · joungho kim · Jinkyoo Park

@
in 3rd Offline Reinforcement Learning Workshop: Offline RL as a "Launchpad" »
Event URL: https://openreview.net/forum?id=FR9NkGgaLw »

This paper proposes \textit{collaborative symmetricity exploitation} (\ourmethod{}) framework to train a solver for the decoupling capacitor placement problem (DPP), one of the significant hardware design problems. Due to the sequentially coupled multi-level property of the hardware design process, the design condition of DPP changes depending on the design of higher-level problems. Also, the online evaluation of real-world electrical performance through simulation is extremely costly. Thus, we propose the \ourmethod{} framework that allows data-efficient offline learning of a DPP solver (i.e., contextualized policy) with high generalization capability over changing task conditions. Leveraging the symmetricity for offline learning of hardware design solver increases data-efficiency by reducing the solution space and improves generalization capability by capturing the invariant nature present regardless of changing conditions. Extensive experiments verified that \ourmethod{} with zero-shot inference outperforms the neural baselines and iterative conventional design methods on the DPP benchmark. Furthermore, \ourmethod{} greatly outperformed the expert method used to generate the offline data for training.

Author Information

HAEYEON KIM (KAIST)
Minsu Kim (Korea Advanced Institute of Science and Technology)
joungho kim (KAIST)
Jinkyoo Park (KAIST)

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