Article

Design and reliability challenges in nanometer technologies

Authors:

Shekhar Borkar,

Tanay Karnik,

Vivek DeAuthors Info & Claims

DAC '04: Proceedings of the 41st annual Design Automation Conference

Page 75

https://doi.org/10.1145/996566.996588

Published: 07 June 2004 Publication History

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Abstract

CMOS technology scaling is causing the channel lengths to be sub-wavelength of light. Parameter variation, caused by sub-wavelength lithography, will pose a major challenge for design and reliability of future high performance microprocessors in nanometer technologies. In this paper, we present the impact of these variations on processor functionality, predictability and reliability. We propose design and CAD solutions for variation tolerance. We conclude this paper with soft error rate scaling trends and soft error tolerant circuits for reliability enhancement.

References

[1]

T. Karnik, S. Borkar, V. De, "Sub 90-nm Technologies-challenges and opportunities for CAD", IEEE ICCAD, pp. 203--206, Nov. 2002.

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[2]

S. Borkar, et al., "Parameter Variations and Impact on Circuits and Microarchitecture," ACM/IEEE DAC, pp. 338--342, June 2003.

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Tan JWang WMa MWei XYan K(2023)Improving the Performance of CNN Accelerator Architecture under the Impact of Process VariationsACM Transactions on Design Automation of Electronic Systems10.1145/360423628:5(1-21)Online publication date: 9-Sep-2023
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Diware SSingh AGebregiorgis AJoshi RHamdioui SBishnoi R(2023)Accurate and Energy-Efficient Bit-Slicing for RRAM-Based Neural NetworksIEEE Transactions on Emerging Topics in Computational Intelligence10.1109/TETCI.2022.31913977:1(164-177)Online publication date: Feb-2023
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Diware SDash SGebregiorgis AJoshi RStrydis CHamdioui SBishnoi R(2023)Severity-Based Hierarchical ECG Classification Using Neural NetworksIEEE Transactions on Biomedical Circuits and Systems10.1109/TBCAS.2023.324268317:1(77-91)Online publication date: Feb-2023
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Index Terms

Design and reliability challenges in nanometer technologies
1. Hardware
  1. Hardware test
  2. Robustness

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Information & Contributors

Information

Published In

DAC '04: Proceedings of the 41st annual Design Automation Conference

June 2004

1002 pages

ISBN:1581138288

DOI:10.1145/996566

General Chair:
Sharad Malik
Princeton University, Princeton, NJ
,
Program Chairs:
Limor Fix
Intel Semiconductors Ltd., Haifa, Israel
,
Andrew B. Kahng
University of California at San Diego, La Jolla, CA

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 June 2004

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DAC04: The 41st Annual Design Automation Conference 2004

June 7 - 11, 2004

CA, San Diego, 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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Tan JWang WMa MWei XYan K(2023)Improving the Performance of CNN Accelerator Architecture under the Impact of Process VariationsACM Transactions on Design Automation of Electronic Systems10.1145/360423628:5(1-21)Online publication date: 9-Sep-2023
https://dl.acm.org/doi/10.1145/3604236
Diware SSingh AGebregiorgis AJoshi RHamdioui SBishnoi R(2023)Accurate and Energy-Efficient Bit-Slicing for RRAM-Based Neural NetworksIEEE Transactions on Emerging Topics in Computational Intelligence10.1109/TETCI.2022.31913977:1(164-177)Online publication date: Feb-2023
https://doi.org/10.1109/TETCI.2022.3191397
Diware SDash SGebregiorgis AJoshi RStrydis CHamdioui SBishnoi R(2023)Severity-Based Hierarchical ECG Classification Using Neural NetworksIEEE Transactions on Biomedical Circuits and Systems10.1109/TBCAS.2023.324268317:1(77-91)Online publication date: Feb-2023
https://doi.org/10.1109/TBCAS.2023.3242683
Khodabandeloo BKhonsari ABehnam PMajidi AHajiesmaili M(2022)Stereo: Assignment and Scheduling in MPSoC Under Process Variation by Combining Stochastic and Decomposition ApproachesIEEE Transactions on Computers10.1109/TC.2022.314184171:11(2940-2954)Online publication date: 1-Nov-2022
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Singh ALebdeh MGebregiorgis ABishnoi RJoshi RHamdioui S(2021)SRIF: Scalable and Reliable Integrate and Fire Circuit ADC for Memristor-Based CIM ArchitecturesIEEE Transactions on Circuits and Systems I: Regular Papers10.1109/TCSI.2021.306121468:5(1917-1930)Online publication date: May-2021
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Last FSchlichtmann USchlichtmann UGal RAmrouch HLi H(2020)Partial Sharing Neural Networks for Multi-Target Regression on Power and Performance of Embedded MemoriesProceedings of the 2020 ACM/IEEE Workshop on Machine Learning for CAD10.1145/3380446.3430642(123-128)Online publication date: 16-Nov-2020
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Romaszkan WLi TGupta P(2020)3PXNetACM Transactions on Embedded Computing Systems10.1145/337115719:1(1-23)Online publication date: 6-Feb-2020
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Azari EVrudhula S(2020)ELSAACM Transactions on Embedded Computing Systems10.1145/336663419:1(1-21)Online publication date: 6-Feb-2020
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Zhang QXu Q(2020)ApproxIt: A Quality Management Framework of Approximate Computing for Iterative MethodsIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2017.277523639:5(991-1002)Online publication date: May-2020
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Implications of fin width scaling on variability and reliability of high-k metal gate FinFETs

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