GCS-Timer: GPU-Accelerated Current Source Model Based Static Timing Analysis

Shiju Lin; Guannan Guo; Tsung Wei Huang; Weihua Sheng; Evangeline Young; Martin Wong

doi:10.1145/3649329.3655983

GCS-Timer: GPU-Accelerated Current Source Model Based Static Timing Analysis

Shiju Lin, Guannan Guo, Tsung Wei Huang, Weihua Sheng, Evangeline Young, Martin Wong

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

11 Citations (Scopus)

Abstract

Composite Current Source (CCS) timing model plays an important role in modern static timing analysis (STA) because it precisely captures the timing behavior of a design at advanced nodes. However, CCS is extremely time-consuming due to its accurate but complicated timing models. To overcome this challenge, we introduce GCS-Timer, a GPU-accelerated CCS-based timing analysis algorithm. Unlike existing methods that perform model order reduction to trade accuracy for speed, GCS-Timer achieves high accuracy through a fast simulation-based analysis using GPU computing. Experimental results show that GCS-Timer can complete CCS analysis with better accuracy and achieve 3.2X faster runtime compared with a 16-threaded industrial standard timer. The source code is available at https://github.com/cuhk-eda/GCS-Timer.

Original language	English
Title of host publication	Proceedings of the 61st ACM/IEEE Design Automation Conference, DAC 2024
Publisher	Association for Computing Machinery (ACM)
Number of pages	6
ISBN (Print)	9798400706011
DOIs	https://doi.org/10.1145/3649329.3655983
Publication status	Published - 7 Nov 2024
Event	61st ACM/IEEE Design Automation Conference, DAC 2024 - San Francisco, San Francisco, United States Duration: 23 Jun 2024 → 27 Jun 2024 https://dl.acm.org/doi/proceedings/10.1145/3649329 (Conference proceedings) https://www.dac.com/

Publication series

Name	Proceedings - Design Automation Conference
ISSN (Print)	0738-100X

Conference

Conference	61st ACM/IEEE Design Automation Conference, DAC 2024
Abbreviated title	DAC 2024
Country/Territory	United States
City	San Francisco
Period	23/06/24 → 27/06/24
Internet address	https://dl.acm.org/doi/proceedings/10.1145/3649329 (Conference proceedings) https://www.dac.com/

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10.1145/3649329.3655983

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title = "GCS-Timer: GPU-Accelerated Current Source Model Based Static Timing Analysis",

abstract = "Composite Current Source (CCS) timing model plays an important role in modern static timing analysis (STA) because it precisely captures the timing behavior of a design at advanced nodes. However, CCS is extremely time-consuming due to its accurate but complicated timing models. To overcome this challenge, we introduce GCS-Timer, a GPU-accelerated CCS-based timing analysis algorithm. Unlike existing methods that perform model order reduction to trade accuracy for speed, GCS-Timer achieves high accuracy through a fast simulation-based analysis using GPU computing. Experimental results show that GCS-Timer can complete CCS analysis with better accuracy and achieve 3.2X faster runtime compared with a 16-threaded industrial standard timer. The source code is available at https://github.com/cuhk-eda/GCS-Timer.",

author = "Shiju Lin and Guannan Guo and Huang, {Tsung Wei} and Weihua Sheng and Evangeline Young and Martin Wong",

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year = "2024",

month = nov,

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doi = "10.1145/3649329.3655983",

language = "English",

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booktitle = "Proceedings of the 61st ACM/IEEE Design Automation Conference, DAC 2024",

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Lin, S, Guo, G, Huang, TW, Sheng, W, Young, E & Wong, M 2024, GCS-Timer: GPU-Accelerated Current Source Model Based Static Timing Analysis. in Proceedings of the 61st ACM/IEEE Design Automation Conference, DAC 2024., 71, Proceedings - Design Automation Conference, Association for Computing Machinery (ACM), 61st ACM/IEEE Design Automation Conference, DAC 2024, San Francisco, United States, 23/06/24. https://doi.org/10.1145/3649329.3655983

GCS-Timer: GPU-Accelerated Current Source Model Based Static Timing Analysis. / Lin, Shiju; Guo, Guannan; Huang, Tsung Wei et al.
Proceedings of the 61st ACM/IEEE Design Automation Conference, DAC 2024. Association for Computing Machinery (ACM), 2024. 71 (Proceedings - Design Automation Conference).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

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T1 - GCS-Timer

T2 - 61st ACM/IEEE Design Automation Conference, DAC 2024

AU - Lin, Shiju

AU - Guo, Guannan

AU - Huang, Tsung Wei

AU - Sheng, Weihua

AU - Young, Evangeline

AU - Wong, Martin

PY - 2024/11/7

Y1 - 2024/11/7

N2 - Composite Current Source (CCS) timing model plays an important role in modern static timing analysis (STA) because it precisely captures the timing behavior of a design at advanced nodes. However, CCS is extremely time-consuming due to its accurate but complicated timing models. To overcome this challenge, we introduce GCS-Timer, a GPU-accelerated CCS-based timing analysis algorithm. Unlike existing methods that perform model order reduction to trade accuracy for speed, GCS-Timer achieves high accuracy through a fast simulation-based analysis using GPU computing. Experimental results show that GCS-Timer can complete CCS analysis with better accuracy and achieve 3.2X faster runtime compared with a 16-threaded industrial standard timer. The source code is available at https://github.com/cuhk-eda/GCS-Timer.

AB - Composite Current Source (CCS) timing model plays an important role in modern static timing analysis (STA) because it precisely captures the timing behavior of a design at advanced nodes. However, CCS is extremely time-consuming due to its accurate but complicated timing models. To overcome this challenge, we introduce GCS-Timer, a GPU-accelerated CCS-based timing analysis algorithm. Unlike existing methods that perform model order reduction to trade accuracy for speed, GCS-Timer achieves high accuracy through a fast simulation-based analysis using GPU computing. Experimental results show that GCS-Timer can complete CCS analysis with better accuracy and achieve 3.2X faster runtime compared with a 16-threaded industrial standard timer. The source code is available at https://github.com/cuhk-eda/GCS-Timer.

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GCS-Timer: GPU-Accelerated Current Source Model Based Static Timing Analysis

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