research-article

Error Propagation Aware Timing Relaxation For Approximate Near Threshold Computing

Authors:

Anteneh Gebregiorgis,

Mehdi B. TahooriAuthors Info & Claims

DAC '17: Proceedings of the 54th Annual Design Automation Conference 2017

Article No.: 77, Pages 1 - 6

https://doi.org/10.1145/3061639.3062240

Published: 18 June 2017 Publication History

Abstract

Near threshold computing (NTC) through aggressive supply voltage scaling has the potential to significantly improve energy-efficiency. However, the increase in variation-induced timing errors is a major challenge in NTC. This can be addressed in the scope of approximate computing by selectively embracing non-important variation-induced timing errors. In this paper, we propose a framework to leverage the error tolerance potential of approximate computing for energy-efficient NTC designs. In our framework, statistical timing error analysis as well as structural and functional error propagation analysis is performed to identify the approximable portion of a design. Then, a mixed-timing logic synthesis is employed to improve energy-efficiency by embracing errors in the approximable portion of the design. Experimental results show that the proposed approach can improve the energy-efficiency of NTC designs by more than 30%.

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Wang ZXu RChen JXiao J(2023)A Survey of Reliability Issues Related to Approximate CircuitsJournal of Computer Science and Technology10.1007/s11390-023-2554-x38:2(273-288)Online publication date: 30-Mar-2023
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cover image ACM Conferences

DAC '17: Proceedings of the 54th Annual Design Automation Conference 2017

June 2017

533 pages

ISBN:9781450349277

DOI:10.1145/3061639

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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EDAC: Electronic Design Automation Consortium
SIGDA: ACM Special Interest Group on Design Automation
IEEE-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

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Published: 18 June 2017

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DAC '17: The 54th Annual Design Automation Conference 2017

June 18 - 22, 2017

TX, Austin, USA

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Overall Acceptance Rate 1,770 of 5,499 submissions, 32%

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Cited By

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https://doi.org/10.1109/AITest58265.2023.00029
Wang ZXu RChen JXiao J(2023)A Survey of Reliability Issues Related to Approximate CircuitsJournal of Computer Science and Technology10.1007/s11390-023-2554-x38:2(273-288)Online publication date: 30-Mar-2023
https://doi.org/10.1007/s11390-023-2554-x
Hanif MShafique M(2023)Deep Learning Reliability: Towards Mitigating Reliability Threats in Deep Learning Systems by Exploiting Intrinsic Characteristics of DNNsEmbedded Machine Learning for Cyber-Physical, IoT, and Edge Computing10.1007/978-3-031-40677-5_21(553-568)Online publication date: 7-Oct-2023
https://doi.org/10.1007/978-3-031-40677-5_21
Ibtesam MSolangi UKim JAnsari MPark S(2022)Reliable Test Architecture With Test Cost Reduction for Systolic-Based DNN AcceleratorsIEEE Transactions on Circuits and Systems II: Express Briefs10.1109/TCSII.2021.310841569:3(1537-1541)Online publication date: Mar-2022
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Ibtesam MSolangi UKim JAnsari MPark S(2022)Highly Efficient Test Architecture for Low-Power AI AcceleratorsIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2021.311073941:8(2728-2738)Online publication date: Aug-2022
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Lin WZhu YArslan T(2022)A Dynamically Reconfigurable Column Streaming-based Convolution Engine for Edge AI Accelerators2022 29th IEEE International Conference on Electronics, Circuits and Systems (ICECS)10.1109/ICECS202256217.2022.9970898(1-4)Online publication date: 24-Oct-2022
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Amanollahi SKamal MAfzali-Kusha APedram M(2020)Circuit-Level Techniques for Logic and Memory Blocks in Approximate Computing SystemsxProceedings of the IEEE10.1109/JPROC.2020.3020792108:12(2150-2177)Online publication date: Dec-2020
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Hanif MKhalid FPutra RTeimoori MKriebel FZhang JLiu KRehman STheocharides TArtusi AGarg SShafique M(2020)Robust Computing for Machine Learning-Based SystemsDependable Embedded Systems10.1007/978-3-030-52017-5_20(479-503)Online publication date: 10-Dec-2020
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Gebregiorgis ATahoori M(2019)Test Pattern Generation for Approximate Circuits Based on Boolean Satisfiability2019 Design, Automation & Test in Europe Conference & Exhibition (DATE)10.23919/DATE.2019.8714898(1028-1033)Online publication date: Mar-2019
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