PhyLS: an AI-driven physically aware synthesis platform

Hongyang PAN , Cunqing LAN , Zhiang WANG , Keren ZHU

Front. Comput. Sci. ›› 2027, Vol. 21 ›› Issue (6) : 2106205

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Front. Comput. Sci. ›› 2027, Vol. 21 ›› Issue (6) :2106205 DOI: 10.1007/s11704-025-51861-4
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PhyLS: an AI-driven physically aware synthesis platform
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Hongyang PAN, Cunqing LAN, Zhiang WANG, Keren ZHU. PhyLS: an AI-driven physically aware synthesis platform. Front. Comput. Sci., 2027, 21(6): 2106205 DOI:10.1007/s11704-025-51861-4

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Alpert C J, Karandikar S K, Li Z, Nam G J, Quay S T, Ren H, Sze C N, Villarrubia P G, Yildiz M C . Techniques for fast physical synthesis. Proceedings of the IEEE, 2007, 95( 3): 573–599

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Agiza A, Reda S. OpenPhySyn: an open-source physical synthesis optimization toolkit. In: Proceedings of Workshop on Open-Source EDA Technology. 2020

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Xie Z, Liang R, Xu X, Hu J, Chang C C, Pan J, Chen Y . Preplacement net length and timing estimation by customized graph neural network. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2022, 41( 11): 4667–4680

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Agiza A, Roy R, Ene T D, Godil S, Reda S, Catanzaro B. GraPhSyM: graph physical synthesis model. In: Proceedings of 2023 IEEE/ACM International Conference on Computer Aided Design. 2023, 1−9

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