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Language Support for Navigating Architecture Design in Closed Form

Authors:

Georgios Tzimpragos,

Joseph Mcmahan,

Deeksha Dangwal,

Nestan Tsiskaridze,

George Michelogiannakis,

Dilip P. Vasudevan,

Timothy SherwoodAuthors Info & Claims

ACM Journal on Emerging Technologies in Computing Systems (JETC), Volume 16, Issue 1

Article No.: 9, Pages 1 - 28

https://doi.org/10.1145/3360047

Published: 25 October 2019 Publication History

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Abstract

As computer architecture continues to expand beyond software-agnostic microarchitecture to specialized and heterogeneous logic or even radically different emerging computing models (e.g., quantum cores, DNA storage units), detailed cycle-level simulation is no longer presupposed. Exploring designs under such complex interacting relationships (e.g., performance, energy, thermal, frequency) calls for a more integrative but higher-level approach. We propose Charm, a modeling language supporting closed-form high-level architecture modeling. Charm enables mathematical representations of mutually dependent architectural relationships to be specified, composed, checked, evaluated, reused, and shared. The language is interpreted through a combination of automatic symbolic evaluation, scalable graph transformation, and efficient compiler techniques, generating executable DAGs and optimized analysis procedures. Charm also exploits the advancements in satisfiability modulo theory solvers to automatically search the design space to help architects explore multiple design knobs simultaneously (e.g., different CNN tiling configurations). Through two case studies, we demonstrate that Charm allows one to define high-level architecture models in a clean and concise format, maximize reusability and shareability, capture unreasonable assumptions, and significantly ease design space exploration at a high level.

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Cited By

Lv PFan JNie XDong WJiang XZhou BXu MXu C(2023)User-Guided Personalized Image Aesthetic Assessment Based on Deep Reinforcement LearningIEEE Transactions on Multimedia10.1109/TMM.2021.313075225(736-749)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1109/TMM.2021.3130752
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https://dl.acm.org/doi/10.1109/TMM.2021.3097900

Index Terms

Language Support for Navigating Architecture Design in Closed Form

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cover image ACM Journal on Emerging Technologies in Computing Systems

ACM Journal on Emerging Technologies in Computing Systems Volume 16, Issue 1

January 2020

232 pages

ISSN:1550-4832

EISSN:1550-4840

DOI:10.1145/3365593

Editor:
Ramesh Karri
Polytechnic Institute of New York University, USA

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Copyright © 2019 ACM.

Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of the United States government. As such, the 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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Publication History

Published: 25 October 2019

Accepted: 01 September 2019

Revised: 01 June 2019

Received: 01 January 2019

Published in JETC Volume 16, Issue 1

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Lv PFan JNie XDong WJiang XZhou BXu MXu C(2023)User-Guided Personalized Image Aesthetic Assessment Based on Deep Reinforcement LearningIEEE Transactions on Multimedia10.1109/TMM.2021.313075225(736-749)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1109/TMM.2021.3130752
Li CZhang PWang C(2022)Harmonious Textual Layout Generation Over Natural Images via Deep Aesthetics LearningIEEE Transactions on Multimedia10.1109/TMM.2021.309790024(3416-3428)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1109/TMM.2021.3097900

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