FastTuner: Transferable Physical Design Parameter Optimization using Fast Reinforcement Learning-Reference-Cited by-同舟云学术

FastTuner: Transferable Physical Design Parameter Optimization using Fast Reinforcement Learning

Published:2024-03-12 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2024 International Symposium on Physical Design
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Author:

Hsiao Hao-Hsiang¹^ORCID,Lu Yi-Chen¹^ORCID,Vanna-Iampikul Pruek¹^ORCID,Lim Sung Kyu¹^ORCID

Affiliation:

1. Georgia Institute of Technology, Atlanta, GA, USA

Funder

Center for Advanced Electronics in Machine Learning

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3626184.3633328

Reference10 articles.

1. Yi-Chen Lu et al. GAN-CTS: A Generative Adversarial Framework for Clock Tree Prediction and Optimization. In 2019 IEEE/ACM International Conference on Computer-Aided Design (ICCAD), pages 1--8, 2019.

2. Anthony Agnesina et al. VLSI Placement Parameter Optimization using Deep Reinforcement Learning. In 2020 IEEE/ACM International Conference On Computer Aided Design (ICCAD), pages 1--9, 2020.

3. Rongjian Liang et al. FlowTuner: A Multi-Stage EDA Flow Tuner Exploiting Parameter Knowledge Transfer. In 2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD), page 1--9. IEEE Press, 2021.

4. Ecenur Ustun et al. LAMDA: Learning-Assisted Multi-stage Autotuning for FPGA Design Closure. In 2019 IEEE 27th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM), pages 74--77, 2019.

5. Yuzhe Ma et al. CAD Tool Design Space Exploration via Bayesian Optimization. In 2019 ACM/IEEE 1st Workshop on Machine Learning for CAD (MLCAD), pages 1--6, 2019.

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