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Dynamic IR-drop ECO optimization by cell movement with current waveform staggering and machine learning guidance

Published: 17 December 2020 Publication History

Abstract

Excessive dynamic IR-drop degrades the circuit performance and may lead to functional failure. Existing IR-drop fixing techniques at the placement stage do not consider the time-variant property and thus cannot handle dynamic IR-drop hotspots well. In current practice, designers perform Engineer Change Order (ECO) to move out these hotspot cells based on their experience. In this paper, we present a novel dynamic IR-drop ECO optimization and prediction framework by wise cell movement. We first spread high demand current cells in a global view to stagger their current waveforms. Then, we further move IR hotspot cells close to power/ground (PG) vias for minimizing the resistance from PG pads to their PG pins. Moreover, we propose an accurate machine learning-based dynamic IR-drop prediction model to guide the final cell movement. The features of our model capture power ground network characteristics, timing information, and cumulative current drawn by cells, thus leading to a general model applicable to ECO. Experimental results show that our proposed model precisely predicts dynamic IR-drop after cell movement, and our optimization scheme can substantially alleviate dynamic IR-drop without timing degradation.

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Cited By

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  • (2024)A Hybrid ECO Detailed Placement Flow for Improved Reduction of Dynamic IR DropProceedings of the Great Lakes Symposium on VLSI 202410.1145/3649476.3658727(390-396)Online publication date: 12-Jun-2024
  • (2024)Power Sub-Mesh Construction in Multiple Power Domain Design with IR Drop and Routability OptimizationProceedings of the 2024 International Symposium on Physical Design10.1145/3626184.3633312(205-212)Online publication date: 12-Mar-2024
  • (2024)UnetPro: Combining Attention with Skip Connection in Unet for Efficient IR Drop Prediction2024 2nd International Symposium of Electronics Design Automation (ISEDA)10.1109/ISEDA62518.2024.10617653(510-515)Online publication date: 10-May-2024
  • Show More Cited By

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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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  • IEEE CAS
  • IEEE CEDA
  • IEEE CS

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New York, NY, United States

Publication History

Published: 17 December 2020

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Author Tags

  1. ECO
  2. IR-drop
  3. detailed placement
  4. machine learning

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  • Research-article

Funding Sources

  • Global Unichip Corp.

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ICCAD '20
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Overall Acceptance Rate 457 of 1,762 submissions, 26%

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Cited By

View all
  • (2024)A Hybrid ECO Detailed Placement Flow for Improved Reduction of Dynamic IR DropProceedings of the Great Lakes Symposium on VLSI 202410.1145/3649476.3658727(390-396)Online publication date: 12-Jun-2024
  • (2024)Power Sub-Mesh Construction in Multiple Power Domain Design with IR Drop and Routability OptimizationProceedings of the 2024 International Symposium on Physical Design10.1145/3626184.3633312(205-212)Online publication date: 12-Mar-2024
  • (2024)UnetPro: Combining Attention with Skip Connection in Unet for Efficient IR Drop Prediction2024 2nd International Symposium of Electronics Design Automation (ISEDA)10.1109/ISEDA62518.2024.10617653(510-515)Online publication date: 10-May-2024
  • (2023)Routability-driven Power/Ground Network Optimization Based on Machine LearningACM Transactions on Design Automation of Electronic Systems10.1145/358781728:4(1-27)Online publication date: 17-May-2023
  • (2023)CircuitNet: An Open-Source Dataset for Machine Learning in VLSI CAD Applications With Improved Domain-Specific Evaluation Metric and Learning StrategiesIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2023.328797042:12(5034-5047)Online publication date: Dec-2023
  • (2023)Risk Propagation Based Vector Profiling for High Coverage Dynamic IR-Drop Analysis2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD)10.1109/ICCAD57390.2023.10323636(1-8)Online publication date: 28-Oct-2023
  • (2023)Design of Digital Integrated Circuits by Improving the Characteristics of Digital CellsMachine Learning-based Design and Optimization of High-Speed Circuits10.1007/978-3-031-50714-4_6(279-336)Online publication date: 31-Dec-2023
  • (2022)Optimizing Design Power Integrity using IR-Aware Placement2022 37th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC)10.1109/ITC-CSCC55581.2022.9894858(1-3)Online publication date: 5-Jul-2022

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