research-article

Dynamic reliability management for near-threshold dark silicon processors

Authors:

Jagadeesh Gaddipati,

Sheldon X.-D. TanAuthors Info & Claims

2016 IEEE/ACM International Conference on Computer-Aided Design (ICCAD)

Pages 1 - 7

https://doi.org/10.1145/2966986.2980080

Published: 07 November 2016 Publication History

Abstract

In this article, we propose a new dynamic reliability management (DRM) techniques at the system level for emerging low power dark silicon manycore microprocessors operating in near-threshold region. We mainly consider the electromigration (EM) failures. To leverage the EM recovery effects, which was ignored in the past, at the system-level, we propose a new equivalent DC current model to consider recovery effects for general time-varying current waveforms so that existing compact EM model can be applied. The new equivalent DC current is calculated in two steps: firstly, the equivalent square waveform is calculated so that peak and terminal stresses are matched, secondly, the parameterized equivalent DC current is derived in terms of the parameters of the periodic fitted square waveforms from the first step. The new recovery EM model can allow EM-induced lifetime to be better managed at the system level. The system level energy optimization problem considering EM lifetime subject to power and performance constraints is framed by seeking the best dark silicon cores' voltage and on/off status. The resulting problem is solved by the State-Action-Reward-State-Action (SARSA) reinforcement learning algorithm. Experimental results on a 64-core near-threshold dark silicon processor show that the new equivalent EM DC currents can fully exhibit the recovery effects at the system-level so that trade-off between EM lifetime and energy/performance can be easily made. We further show that the proposed learning-based energy optimization can effectively manage and optimize energy subject to reliability, given power budget and performance limits. When the recovery effects are considered, the new optimization method can achieve 8.6× longer lifetime at the costs of 2.0× more energy and 3.3× more performance degradation.

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2016 IEEE/ACM International Conference on Computer-Aided Design (ICCAD)

Nov 2016

946 pages

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Published: 07 November 2016

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Cited By

Ranjbar BAlikhani HSafaei BEjlali AKumar A(2022)Learning-Oriented QoS- and Drop-Aware Task Scheduling for Mixed-Criticality SystemsComputers10.3390/computers1107010111:7(101)Online publication date: 22-Jun-2022
https://doi.org/10.3390/computers11070101
Dilek SSmri RTosun SDal D(2022)A High-Level Synthesis Methodology for Energy and Reliability-Oriented DesignsIEEE Transactions on Computers10.1109/TC.2020.304388571:1(161-174)Online publication date: 1-Jan-2022
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Majzoub SSaleh RTaouil MHamdioui SBamakhrama M(2022)Rapid Design-Space Exploration for Low-Power Manycores Under Process Variation Utilizing Machine LearningIEEE Access10.1109/ACCESS.2022.318714010(70187-70203)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3187140
Wang SKim TSun ZTan STahoori M(2018)Recovery-Aware Proactive TSV Repair for Electromigration Lifetime Enhancement in 3-D ICsIEEE Transactions on Very Large Scale Integration (VLSI) Systems10.1109/TVLSI.2017.277558626:3(531-543)Online publication date: 1-Mar-2018
https://dl.acm.org/doi/10.1109/TVLSI.2017.2775586
Tan SAmrouch HKim TSun ZCook CHenkel J(2018)Recent advances in EM and BTI induced reliability modeling, analysis and optimization (invited)Integration, the VLSI Journal10.1016/j.vlsi.2017.08.00960:C(132-152)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.1016/j.vlsi.2017.08.009
Wang SZhao HTan STahoori M(2017)Recovery-aware proactive TSV repair for electromigration in 3D ICsProceedings of the Conference on Design, Automation & Test in Europe10.5555/3130379.3130429(220-225)Online publication date: 27-Mar-2017
https://dl.acm.org/doi/10.5555/3130379.3130429
Wang SZhao HTan STahoori M(2017)Recovery-aware proactive TSV repair for electromigration in 3D ICsDesign, Automation & Test in Europe Conference & Exhibition (DATE), 201710.23919/DATE.2017.7926986(220-225)Online publication date: Mar-2017
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