research-article

Towards collaborative intelligence: routability estimation based on decentralized private data

Authors:

Chen-Chia Chang,

Yiran ChenAuthors Info & Claims

DAC '22: Proceedings of the 59th ACM/IEEE Design Automation Conference

Pages 961 - 966

https://doi.org/10.1145/3489517.3530578

Published: 23 August 2022 Publication History

Abstract

Applying machine learning (ML) in design flow is a popular trend in Electronic Design Automation (EDA) with various applications from design quality predictions to optimizations. Despite its promise, which has been demonstrated in both academic researches and industrial tools, its effectiveness largely hinges on the availability of a large amount of high-quality training data. In reality, EDA developers have very limited access to the latest design data, which is owned by design companies and mostly confidential. Although one can commission ML model training to a design company, the data of a single company might be still inadequate or biased, especially for small companies. Such data availability problem is becoming the limiting constraint on future growth of ML for chip design. In this work, we propose an Federated-Learning based approach for well-studied ML applications in EDA. Our approach allows an ML model to be collaboratively trained with data from multiple clients but without explicit access to the data for respecting their data privacy. To further strengthen the results, we co-design a customized ML model FLNet and its personalization under the decentralized training scenario. Experiments on a comprehensive dataset show that collaborative training improves accuracy by 11% compared with individual local models, and our customized model FLNet significantly outperforms the best of previous routability estimators in this collaborative training flow.

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Rao KShaik FKrishna MBhavya STeja PBasha S(2024)Food Detection with Image Processing Using Convolutional Neural Network (CNN)Modern Approaches in Machine Learning and Cognitive Science: A Walkthrough10.1007/978-3-031-43009-1_13(141-146)Online publication date: 14-Jan-2024
https://doi.org/10.1007/978-3-031-43009-1_13
Karimullah SVishnuvardhan DGunjan VShaik F(2024)Optimization of Area and Wirelength Using Hybrid BPSO Algorithm in VLSI Floorplan and Placement for IC DesignModern Approaches in Machine Learning and Cognitive Science: A Walkthrough10.1007/978-3-031-43009-1_11(121-130)Online publication date: 14-Jan-2024
https://doi.org/10.1007/978-3-031-43009-1_11
Babu JSuresh YRani RYasmeen SReddy KHarshavardhan K(2024)Accelerated Addition in Resistive Ram Array Using Parallel-Friendly Majority GatesModern Approaches in Machine Learning and Cognitive Science: A Walkthrough10.1007/978-3-031-43009-1_10(111-119)Online publication date: 14-Jan-2024
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Index Terms

Towards collaborative intelligence: routability estimation based on decentralized private data
1. Computing methodologies
  1. Machine learning
2. Hardware
  1. Electronic design automation
    1. Physical design (EDA)

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cover image ACM Conferences

DAC '22: Proceedings of the 59th ACM/IEEE Design Automation Conference

July 2022

1462 pages

ISBN:9781450391429

DOI:10.1145/3489517

General Chair:
Rob Oshana
NXP

Copyright © 2022 ACM.

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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SIGDA: ACM Special Interest Group on Design Automation
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New York, NY, United States

Publication History

Published: 23 August 2022

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DAC '22

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DAC '22: 59th ACM/IEEE Design Automation Conference

July 10 - 14, 2022

California, San Francisco

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Overall Acceptance Rate 1,770 of 5,499 submissions, 32%

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62nd ACM/IEEE Design Automation Conference

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Cited By

Rao KShaik FKrishna MBhavya STeja PBasha S(2024)Food Detection with Image Processing Using Convolutional Neural Network (CNN)Modern Approaches in Machine Learning and Cognitive Science: A Walkthrough10.1007/978-3-031-43009-1_13(141-146)Online publication date: 14-Jan-2024
https://doi.org/10.1007/978-3-031-43009-1_13
Karimullah SVishnuvardhan DGunjan VShaik F(2024)Optimization of Area and Wirelength Using Hybrid BPSO Algorithm in VLSI Floorplan and Placement for IC DesignModern Approaches in Machine Learning and Cognitive Science: A Walkthrough10.1007/978-3-031-43009-1_11(121-130)Online publication date: 14-Jan-2024
https://doi.org/10.1007/978-3-031-43009-1_11
Babu JSuresh YRani RYasmeen SReddy KHarshavardhan K(2024)Accelerated Addition in Resistive Ram Array Using Parallel-Friendly Majority GatesModern Approaches in Machine Learning and Cognitive Science: A Walkthrough10.1007/978-3-031-43009-1_10(111-119)Online publication date: 14-Jan-2024
https://doi.org/10.1007/978-3-031-43009-1_10
Qiu YXing YZheng XGao PCai SXiong X(2023)Progress of Placement Optimization for Accelerating VLSI Physical DesignElectronics10.3390/electronics1202033712:2(337)Online publication date: 9-Jan-2023
https://doi.org/10.3390/electronics12020337
Xie ZPan JChang CHu JChen Y(2023)The Dark Side: Security and Reliability Concerns in Machine Learning for EDAIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2022.319917242:4(1171-1184)Online publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1109/TCAD.2022.3199172
Xie ZPan JChang CLiang RBarboza EChen Y(2022)Deep Learning for RoutabilityMachine Learning Applications in Electronic Design Automation10.1007/978-3-031-13074-8_2(35-61)Online publication date: 10-Aug-2022
https://doi.org/10.1007/978-3-031-13074-8_2

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