IR drop prediction of ECO-revised circuits using machine learning

Lin, Shih-Yao; Fang, Yen-Chun; Li, Yu-Ching; Liu, Yucheng; Yang, Tsung-Shan; Lin, Shang-Chien; Li, Chien-Mo; Fang, Eric Jia-Wei

doi:10.1109/vts.2018.8368657

Cited by 26 publications

(16 citation statements)

References 14 publications

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“…A number of numerical techniques have been well developed and can perform IR drop analysis well on power grids, such as hierarchical methods, random walk methods, Krylov-subspace methods, multi-grid techniques, and vectorless veri cation methods. To further speed up the IR drop analysis, several machine learning based IR drop estimation/prediction methods have been proposed [14,17,21,28]. Those methods typically aim to replace the standard full-chip IR drop analysis tool such as ANSYS RedHawk, via data-based learning and feature selections.…”

Section: Machine Learning Based Ir Drop Analysis and Estimationmentioning

confidence: 99%

“…Those methods typically aim to replace the standard full-chip IR drop analysis tool such as ANSYS RedHawk, via data-based learning and feature selections. For instance, Lin et al [21] proposed full-chip dynamic IR drop analysis based on some power and physical features extracted from cells and layouts. Fang et al [14] tried to improve the scalability by training the models for localized region of layout.…”

Section: Machine Learning Based Ir Drop Analysis and Estimationmentioning

confidence: 99%

“…Hence, there is a need for fast incremental EM-induced IR drop analysis for ECO like xing for the PDN layout via proper sizing of some wire segments [8]. We want to point out that such fast EM-induced IR drop analysis is di erent than the traditional fast static and dynamic IR drop analysis methods [14,17,21,28] as those methods mainly target at accelerating the design time dynamic IR drop analysis tool such as ANSYS Red-Hawk based on the power and timing information of the standard cells.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

GridNet

Zhou

Jin

Tan

2020

Proceedings of the 39th International Conference on Computer-Aided Design

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Section: Machine Learning Based Ir Drop Analysis and Estimationmentioning

confidence: 99%

Section: Machine Learning Based Ir Drop Analysis and Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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GridNet

Zhou

Jin

Tan

2020

Proceedings of the 39th International Conference on Computer-Aided Design

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“…Prior acceleration methods such as [22] trade off accuracy for speed by increasing the coarseness of element discretization. However, the advent of machine learning (ML) has presented fast and accurate solutions to these problems [15,18,19,23,30,37,38,42], trained on simulation data from systems of millions of nodes.…”

mentioning

confidence: 99%

“…For PDN analysis, the works in [15,23] address incremental analysis and are not intended for full-chip estimation.…”

mentioning

confidence: 99%

Encoder-Decoder Networks for Analyzing Thermal and Power Delivery Networks

Chhabria¹,

Ahuja²,

Prabhu³

et al. 2021

Preprint

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Power delivery network (PDN) analysis and thermal analysis are computationally expensive tasks that are essential for successful IC design. Algorithmically, both these analyses have similar computational structure and complexity as they involve the solution to a partial differential equation of the same form. This paper converts these analyses into image-to-image and sequence-to-sequence translation tasks, which allows leveraging a class of machine learning models with an encoder-decoder-based generative (EDGe) architecture to address the time-intensive nature of these tasks. For PDN analysis, we propose two networks: (i) IREDGe: a full-chip static and dynamic IR drop predictor and (ii) EMEDGe: electromigration (EM) hotspot classifier based on input power, power grid distribution, and power pad distribution patterns. For thermal analysis, we propose ThermEDGe, a full-chip static and dynamic temperature estimator based on input power distribution patterns for thermal analysis. These networks are transferable across designs synthesized within the same technology and packing solution. The networks predict on-chip IR drop, EM hotspot locations, and temperature in milliseconds with negligibly small errors against commercial tools requiring several hours.

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Deep Learning for Analyzing Power Delivery Networks and Thermal Networks

Chhabria

Sapatnekar

2022

Machine Learning Applications in Electronic Design Automation

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IR drop prediction of ECO-revised circuits using machine learning

Cited by 26 publications

References 14 publications

GridNet

GridNet

Encoder-Decoder Networks for Analyzing Thermal and Power Delivery Networks

Deep Learning for Analyzing Power Delivery Networks and Thermal Networks

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