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Design Space Exploration of Reinforcement Learning Configurations for Post-Legalization Placement — arXiv:2610.20224

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.20224
License (as archived): CC0-1.0
Archived: 2026-10-15T20:02:27.724Z
SHA-256 of the archived text: df8ce3f11b6e338662b1aa0e792eabb2e5c679707e1f13dce7665f3e9002f23b
Stable URL of this page: https://metraipolis.com/documento/dc_76172dfd6ab0

How to cite

Design Space Exploration of Reinforcement Learning Configurations for Post-Legalization Placement — arXiv:2610.20224 — arXiv (en), https://arxiv.org/abs/2610.20224 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_76172dfd6ab0 (Metraipolis archive dc_76172dfd6ab0, sha256 df8ce3f11b6e338662b1aa0e792eabb2e5c679707e1f13dce7665f3e9002f23b)

BibTeX

@misc{dc76172dfd6ab0,
  title = {Design Space Exploration of Reinforcement Learning Configurations for Post-Legalization Placement — arXiv:2610.20224},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.20224},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_76172dfd6ab0; sha256 df8ce3f11b6e338662b1aa0e792eabb2e5c679707e1f13dce7665f3e9002f23b},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.20224. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Graph Neural Networks (GNNs) and Reinforcement Learning (RL) have recently been explored for VLSI placement optimization, but their effectiveness for post-legalization placement refinement remains unclear. This paper presents a systematic design space exploration of a GNN-Proximal Policy Optimization (GNN-PPO) framework across three ISPD2015 benchmarks. We evaluate 18 configurations covering reward functions, GNN architectures, action spaces, candidate-set sizes, and training strategies. We analyse the results we obtain to identify pitfalls and to assess the prospects and opportunities of the RL methods for post-legalization placement. On top of that we propose a design classification approach to guide the design decisions of RL models.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2610.20224 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-15T19:35:48.299Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).