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arXiv · 2610.20224

Design Space Exploration of Reinforcement Learning Configurations for Post-Legalization Placement

Foteini Oikonomou·Maria Pantazi-Kypraiou·George Goudroumanis·Pavlos Stoikos·Athanasios Tziouvaras·Christos Anagnostopoulos·Georgios Karakonstantis·George Floros
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Abstract

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.

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Foteini Oikonomou, Maria Pantazi-Kypraiou, George Goudroumanis, Pavlos Stoikos, Athanasios Tziouvaras, Christos Anagnostopoulos, Georgios Karakonstantis, George Floros. 2026-10-14. Design Space Exploration of Reinforcement Learning Configurations for Post-Legalization Placement. https://arxiv.org/abs/2610.20224

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