LibAbs: An Efficient and Accurate Timing Macro-Modeling Algorithm for Large Hierarchical Designs

Tin-Yin Lai; Tsung-Wei Huang; Martin D. F. Wong

doi:10.1145/3061639.3062274

LibAbs: An Efficient and Accurate Timing Macro-Modeling Algorithm for Large Hierarchical Designs

Tin-Yin Lai, Tsung-Wei Huang, Martin D. F. Wong

Office of the Provost

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

11 Citations (Scopus)

Abstract

The ever-increasing design complexity is driving the need of fast and accurate macro-modeling algorithms to accelerate the hierarchical timing. We introduce LibAbs, an effective macro-modeling algorithm that efficiently supports high accuracy, high compression rate, and multi-threading. LibAbs applies tree-based graph reduction techniques to reduce the model size with comparable accuracy values to the flat model under multi-threaded environment. LibAbs outperforms existing tools including top winners from TAU 2016 macro-modeling contest in terms of model size, accuracy, and runtime on industry benchmarks. The in-context usage of our abstracted model has also demonstrated promising performance for timing-driven optimizations in large hierarchical designs.

Original language	English
Title of host publication	54th ACM/IEEE Design Automation Conference - Proceedings
Publisher	Association for Computing Machinery (ACM)
Pages	1–6
Number of pages	6
ISBN (Electronic)	9781450349277
DOIs	https://doi.org/10.1145/3061639.3062274
Publication status	Published - Jun 2017
Event	54th ACM/IEEE Design Automation Conference, DAC 2017 - Austin, United States Duration: 18 Jun 2017 → 22 Jun 2017 https://dl.acm.org/doi/proceedings/10.1145/3061639 (Conference proceedings) https://ieeexplore.ieee.org/xpl/conhome/8024229/proceeding (Conference proceedings)

Publication series

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

Conference

Conference	54th ACM/IEEE Design Automation Conference, DAC 2017
Country/Territory	United States
City	Austin
Period	18/06/17 → 22/06/17
Internet address	https://dl.acm.org/doi/proceedings/10.1145/3061639 (Conference proceedings) https://ieeexplore.ieee.org/xpl/conhome/8024229/proceeding (Conference proceedings)

User-Defined Keywords

Timing macro-modeling
Static timing analysis

Access to Document

10.1145/3061639.3062274

Cite this

@inbook{f0d44b7f571a4a4da7b429914e81eaf0,

title = "LibAbs: An Efficient and Accurate Timing Macro-Modeling Algorithm for Large Hierarchical Designs",

abstract = "The ever-increasing design complexity is driving the need of fast and accurate macro-modeling algorithms to accelerate the hierarchical timing. We introduce LibAbs, an effective macro-modeling algorithm that efficiently supports high accuracy, high compression rate, and multi-threading. LibAbs applies tree-based graph reduction techniques to reduce the model size with comparable accuracy values to the flat model under multi-threaded environment. LibAbs outperforms existing tools including top winners from TAU 2016 macro-modeling contest in terms of model size, accuracy, and runtime on industry benchmarks. The in-context usage of our abstracted model has also demonstrated promising performance for timing-driven optimizations in large hierarchical designs.",

keywords = "Timing macro-modeling, Static timing analysis",

author = "Tin-Yin Lai and Tsung-Wei Huang and Wong, {Martin D. F.}",

note = "Funding Information: This work is sponsored by the National Science Foundation under Grant CFF-1320585 and CFF-1421563. Publisher Copyright: {\textcopyright} 2017 ACM; 54th ACM/IEEE Design Automation Conference, DAC 2017 ; Conference date: 18-06-2017 Through 22-06-2017",

year = "2017",

month = jun,

doi = "10.1145/3061639.3062274",

language = "English",

series = "ACM/IEEE Design Automation Conference - Proceedings",

publisher = "Association for Computing Machinery (ACM)",

pages = "1–6",

booktitle = "54th ACM/IEEE Design Automation Conference - Proceedings",

address = "United States",

url = "https://dl.acm.org/doi/proceedings/10.1145/3061639, https://ieeexplore.ieee.org/xpl/conhome/8024229/proceeding",

}

Lai, T-Y, Huang, T-W & Wong, MDF 2017, LibAbs: An Efficient and Accurate Timing Macro-Modeling Algorithm for Large Hierarchical Designs. in 54th ACM/IEEE Design Automation Conference - Proceedings. ACM/IEEE Design Automation Conference - Proceedings, Association for Computing Machinery (ACM), pp. 1–6, 54th ACM/IEEE Design Automation Conference, DAC 2017, Austin, Texas, United States, 18/06/17. https://doi.org/10.1145/3061639.3062274

LibAbs: An Efficient and Accurate Timing Macro-Modeling Algorithm for Large Hierarchical Designs. / Lai, Tin-Yin; Huang, Tsung-Wei; Wong, Martin D. F.
54th ACM/IEEE Design Automation Conference - Proceedings. Association for Computing Machinery (ACM), 2017. p. 1–6 (ACM/IEEE Design Automation Conference - Proceedings).

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

TY - CHAP

T1 - LibAbs: An Efficient and Accurate Timing Macro-Modeling Algorithm for Large Hierarchical Designs

AU - Lai, Tin-Yin

AU - Huang, Tsung-Wei

AU - Wong, Martin D. F.

PY - 2017/6

Y1 - 2017/6

N2 - The ever-increasing design complexity is driving the need of fast and accurate macro-modeling algorithms to accelerate the hierarchical timing. We introduce LibAbs, an effective macro-modeling algorithm that efficiently supports high accuracy, high compression rate, and multi-threading. LibAbs applies tree-based graph reduction techniques to reduce the model size with comparable accuracy values to the flat model under multi-threaded environment. LibAbs outperforms existing tools including top winners from TAU 2016 macro-modeling contest in terms of model size, accuracy, and runtime on industry benchmarks. The in-context usage of our abstracted model has also demonstrated promising performance for timing-driven optimizations in large hierarchical designs.

AB - The ever-increasing design complexity is driving the need of fast and accurate macro-modeling algorithms to accelerate the hierarchical timing. We introduce LibAbs, an effective macro-modeling algorithm that efficiently supports high accuracy, high compression rate, and multi-threading. LibAbs applies tree-based graph reduction techniques to reduce the model size with comparable accuracy values to the flat model under multi-threaded environment. LibAbs outperforms existing tools including top winners from TAU 2016 macro-modeling contest in terms of model size, accuracy, and runtime on industry benchmarks. The in-context usage of our abstracted model has also demonstrated promising performance for timing-driven optimizations in large hierarchical designs.

KW - Timing macro-modeling

KW - Static timing analysis

UR - https://dl.acm.org/doi/10.1145/3061639.3062274

UR - https://ieeexplore.ieee.org/document/8060437

UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85023639335&origin=inward

U2 - 10.1145/3061639.3062274

DO - 10.1145/3061639.3062274

M3 - Chapter

T3 - ACM/IEEE Design Automation Conference - Proceedings

SP - 1

EP - 6

BT - 54th ACM/IEEE Design Automation Conference - Proceedings

PB - Association for Computing Machinery (ACM)

T2 - 54th ACM/IEEE Design Automation Conference, DAC 2017

Y2 - 18 June 2017 through 22 June 2017

ER -

LibAbs: An Efficient and Accurate Timing Macro-Modeling Algorithm for Large Hierarchical Designs

Abstract

Publication series

Conference

User-Defined Keywords

Access to Document

Other files and links

Fingerprint

Cite this