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VLSI placement parameter optimization using deep reinforcement learning

Published: 17 December 2020 Publication History

Abstract

The quality of placement is essential in the physical design flow. To achieve PPA goals, a human engineer typically spends a considerable amount of time tuning the multiple settings of a commercial placer (e.g. maximum density, congestion effort, etc.). This paper proposes a deep reinforcement learning (RL) framework to optimize the placement parameters of a commercial EDA tool. We build an autonomous agent that learns to tune parameters optimally without human intervention and domain knowledge, trained solely by RL from self-search. To generalize to unseen netlists, we use a mixture of handcrafted features from graph topology theory along with graph embeddings generated using unsupervised Graph Neural Networks. Our RL algorithms are chosen to overcome the sparsity of data and latency of placement runs. Our trained RL agent achieves up to 11% and 2.5% wirelength improvements on unseen netlists compared with a human engineer and a state-of-the-art tool auto-tuner, in just one placement iteration (20× and 50× less iterations).

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  • (2024)Efficient Usage of Energy Infrastructure in Smart City Using Machine LearningEAI Endorsed Transactions on Internet of Things10.4108/eetiot.536310Online publication date: 11-Mar-2024
  • (2024)A Deep Reinforcement Learning Floorplanning Algorithm Based on Sequence PairsApplied Sciences10.3390/app1407290514:7(2905)Online publication date: 29-Mar-2024
  • (2024)Optimization of Analog Circuit Placement: A Graph Neural Network ApproachProceedings of the 2024 10th International Conference on Computing and Artificial Intelligence10.1145/3669754.3669815(399-403)Online publication date: 26-Apr-2024
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  1. VLSI placement parameter optimization using deep reinforcement learning

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    Published In

    cover image ACM Conferences
    ICCAD '20: Proceedings of the 39th International Conference on Computer-Aided Design
    November 2020
    1396 pages
    ISBN:9781450380263
    DOI:10.1145/3400302
    • General Chair:
    • Yuan Xie
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    New York, NY, United States

    Publication History

    Published: 17 December 2020

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    Cited By

    View all
    • (2024)Efficient Usage of Energy Infrastructure in Smart City Using Machine LearningEAI Endorsed Transactions on Internet of Things10.4108/eetiot.536310Online publication date: 11-Mar-2024
    • (2024)A Deep Reinforcement Learning Floorplanning Algorithm Based on Sequence PairsApplied Sciences10.3390/app1407290514:7(2905)Online publication date: 29-Mar-2024
    • (2024)Optimization of Analog Circuit Placement: A Graph Neural Network ApproachProceedings of the 2024 10th International Conference on Computing and Artificial Intelligence10.1145/3669754.3669815(399-403)Online publication date: 26-Apr-2024
    • (2024)An Open-Source ML-Based Full-Stack Optimization Framework for Machine Learning AcceleratorsACM Transactions on Design Automation of Electronic Systems10.1145/366465229:4(1-33)Online publication date: 9-Jul-2024
    • (2024)GAN-Place: Advancing Open Source Placers to Commercial-quality Using Generative Adversarial Networks and Transfer LearningACM Transactions on Design Automation of Electronic Systems10.1145/363646129:2(1-17)Online publication date: 14-Feb-2024
    • (2024)DSO.ai - A Distributed System to Optimize Physical Design FlowsProceedings of the 2024 International Symposium on Physical Design10.1145/3626184.3639780(115-116)Online publication date: 12-Mar-2024
    • (2024)ISOP+: Machine Learning-Assisted Inverse Stack-Up Optimization for Advanced Package DesignIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2023.330593443:1(2-15)Online publication date: Jan-2024
    • (2024)Opportunities and challenges of graph neural networks in electrical engineeringNature Reviews Electrical Engineering10.1038/s44287-024-00076-z1:8(529-546)Online publication date: 5-Aug-2024
    • (2024)An efficient leakage power optimization framework based on reinforcement learning with graph neural networkScientific Reports10.1038/s41598-024-76859-z14:1Online publication date: 2-Nov-2024
    • (2023)DevFormerProceedings of the 40th International Conference on Machine Learning10.5555/3618408.3619086(16541-16566)Online publication date: 23-Jul-2023
    • Show More Cited By

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