aiqbench analysis
The study tests whether graph neural network and reinforcement learning methods can refine chip layouts after legalization, a step where gains from prior approaches are less clear. Its 18-configuration sweep across reward functions, architectures, and action spaces is the useful part for readers, since it reports pitfalls alongside prospects. Readers should treat the results as benchmark-specific (three ISPD2015 circuits) and check whether the proposed design classification generalizes beyond them.
Design Space Exploration of Reinforcement Learning Configurations for Post-Legalization Placement
arXiv:2610.20224v1 Announce Type: new 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.
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.
Artificial Intelligence (cs.AI)
arXiv:2610.20224 [cs.AI]
(or arXiv:2610.20224v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.20224 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 195 words
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