research-article

A modular digital VLSI flow for high-productivity SoC design

Authors:

Brucek Khailany,

Rangharajan Venkatesan,

Matthew Fojtik,

Alicia Klinefelter,

Michael Pellauer,

Nathaniel Pinckney,

Yakun Sophia Shao,

Shreesha Srinath,

Christopher Torng,

Sam (Likun) Xi,

Brian ZimmerAuthors Info & Claims

DAC '18: Proceedings of the 55th Annual Design Automation Conference

Article No.: 72, Pages 1 - 6

https://doi.org/10.1145/3195970.3199846

Published: 24 June 2018 Publication History

Abstract

A high-productivity digital VLSI flow for designing complex SoCs is presented. The flow includes high-level synthesis tools, an object-oriented library of synthesizable SystemC and C++ components, and a modular VLSI physical design approach based on fine-grained globally asynchronous locally synchronous (GALS) clocking. The flow was demonstrated on a 16nm FinFET testchip targeting machine learning and computer vision.

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Published In

cover image ACM Conferences

DAC '18: Proceedings of the 55th Annual Design Automation Conference

June 2018

1089 pages

ISBN:9781450357005

DOI:10.1145/3195970

Copyright © 2018 ACM.

© 2018 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of the United States government. As such, the United States Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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EDAC: Electronic Design Automation Consortium
SIGDA: ACM Special Interest Group on Design Automation
IEEE Council on Electronic Design Automation (CEDA)

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SIGBED: ACM Special Interest Group on Embedded Systems

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 June 2018

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DAC '18

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DAC '18: The 55th Annual Design Automation Conference 2018

June 24 - 29, 2018

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

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Tambe TYang EKo GChai YHooper CDonato MWhatmough PRush ABrooks DWei G(2023)A 16-nm SoC for Noise-Robust Speech and NLP Edge AI Inference With Bayesian Sound Source Separation and Attention-Based DNNsIEEE Journal of Solid-State Circuits10.1109/JSSC.2022.317930358:2(569-581)Online publication date: Feb-2023
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Kung HNatesh VSabot Ade Supinski BHall MGamblin T(2021)CAKEProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis10.1145/3458817.3476166(1-14)Online publication date: 14-Nov-2021
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