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Aging- and Variation-Aware Delay Monitoring Using Representative Critical Path Selection

Authors:

Farshad Firouzi,

Krishnendu Chakrabarty,

Mehdi B. TahooriAuthors Info & Claims

ACM Transactions on Design Automation of Electronic Systems (TODAES), Volume 20, Issue 3

Article No.: 39, Pages 1 - 23

https://doi.org/10.1145/2746237

Published: 24 June 2015 Publication History

Abstract

Process together with runtime variations in temperature and voltage, as well as transistor aging, degrade path delay and may eventually induce circuit failure due to timing variations. Therefore, in-field tracking of path delays is essential, and to respond to this need, several delay sensor designs have been proposed in the literature. However, due to the significant overhead of these sensors and the large number of critical paths in today's IC, it is infeasible to monitor the delay of every critical path in silicon. We present an aging- and variationaware representative path selection technique based on machine learning that allows to measure the delay of a small set of paths and infer the delay of a larger pool of paths that are likely to fail due to delay variations. Simulation results for benchmark circuits highlight the accuracy of the proposed approach for predicting critical-path delay based on the selected representative paths.

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Cited By

Chen YYang HLin I(2024)GNN-Based INC and IVC Co-Optimization for Aging Mitigation2024 IEEE European Test Symposium (ETS)10.1109/ETS61313.2024.10567322(1-4)Online publication date: 20-May-2024
https://doi.org/10.1109/ETS61313.2024.10567322
Li DLiang HZhang HWang YYi MLu YHuang Z(2023)BPath-RO: A Performance- and Area-Efficient In Situ Delay Measurement Scheme for Digital ICElectronics10.3390/electronics1223485312:23(4853)Online publication date: 30-Nov-2023
https://doi.org/10.3390/electronics12234853
Li ZHuang ZWang QWang JLuo N(2022)Implementation of Aging Mechanism Analysis and Prediction for XILINX 7-Series FPGAs with a 28-nm ProcessSensors10.3390/s2212443922:12(4439)Online publication date: 12-Jun-2022
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Index Terms

Aging- and Variation-Aware Delay Monitoring Using Representative Critical Path Selection
1. Hardware
  1. Robustness

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Published In

cover image ACM Transactions on Design Automation of Electronic Systems

ACM Transactions on Design Automation of Electronic Systems Volume 20, Issue 3

June 2015

345 pages

ISSN:1084-4309

EISSN:1557-7309

DOI:10.1145/2796316

Editor:
Naehyuck Chang
Korea Advanced Institute of Science and Technology, Korea

Issue’s Table of Contents

Copyright © 2015 ACM.

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Association for Computing Machinery

New York, NY, United States

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ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 24 June 2015

Accepted: 01 March 2015

Revised: 01 February 2015

Received: 01 July 2014

Published in TODAES Volume 20, Issue 3

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Cited By

Chen YYang HLin I(2024)GNN-Based INC and IVC Co-Optimization for Aging Mitigation2024 IEEE European Test Symposium (ETS)10.1109/ETS61313.2024.10567322(1-4)Online publication date: 20-May-2024
https://doi.org/10.1109/ETS61313.2024.10567322
Li DLiang HZhang HWang YYi MLu YHuang Z(2023)BPath-RO: A Performance- and Area-Efficient In Situ Delay Measurement Scheme for Digital ICElectronics10.3390/electronics1223485312:23(4853)Online publication date: 30-Nov-2023
https://doi.org/10.3390/electronics12234853
Li ZHuang ZWang QWang JLuo N(2022)Implementation of Aging Mechanism Analysis and Prediction for XILINX 7-Series FPGAs with a 28-nm ProcessSensors10.3390/s2212443922:12(4439)Online publication date: 12-Jun-2022
https://doi.org/10.3390/s22124439
Varghese MMuthumanickam T(2022)Machine Learning Approaches for Electronic Design Automation in IC Design Flow2022 Sixth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)10.1109/I-SMAC55078.2022.9987302(528-533)Online publication date: 10-Nov-2022
https://doi.org/10.1109/I-SMAC55078.2022.9987302
Wang DLin PYang HHsu CHuang SLin M(2021)A Novel Machine-Learning based SoC Performance Monitoring Methodology under Wide-Range PVT Variations with Unknown Critical Paths2021 58th ACM/IEEE Design Automation Conference (DAC)10.1109/DAC18074.2021.9586155(1370-1371)Online publication date: 5-Dec-2021
https://doi.org/10.1109/DAC18074.2021.9586155
Javvaji PTragoudas S(2020)Test Pattern Generation and Critical Path Selection in the Presence of Statistical DelaysIEEE Transactions on Very Large Scale Integration (VLSI) Systems10.1109/TVLSI.2019.294142628:1(163-173)Online publication date: Jan-2020
https://doi.org/10.1109/TVLSI.2019.2941426
Yu LRen JLu XWang X(2020)NBTI and HCI Aging Prediction and Reliability Screening During Production TestIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2019.296132939:10(3000-3011)Online publication date: Oct-2020
https://doi.org/10.1109/TCAD.2019.2961329
Ebrahimi MNavabi Z(2020)Selecting Representative Critical Paths for Sensor Placement Provides Early FPGA Aging InformationIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2019.295317439:10(2976-2989)Online publication date: Oct-2020
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Ali GBagheriye LKerkhoff H(2020)On-Chip Embedded Instruments Data Fusion and Life-Time Prognostics of Dependable VLSI-SoCs using Machine-Learning2020 IEEE International Symposium on Circuits and Systems (ISCAS)10.1109/ISCAS45731.2020.9180773(1-5)Online publication date: Oct-2020
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Bagheriye LAli GKerkhoff H(2020)Life-Time Prognostics of Dependable VLSI-SoCs using Machine-learning2020 IEEE 26th International Symposium on On-Line Testing and Robust System Design (IOLTS)10.1109/IOLTS50870.2020.9159753(1-4)Online publication date: Jul-2020
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