Efficient systolic-array redundancy architecture for offline/online repair

Keewon Cho; Ingeol Lee; Hyeonchan Lim; Sungho Kang

doi:10.3390/electronics9020338

Efficient systolic-array redundancy architecture for offline/online repair

Keewon Cho, Ingeol Lee, Hyeonchan Lim, Sungho Kang

Department of Electrical and Electronic Engineering

Research output: Contribution to journal › Article › peer-review

Abstract

Neural-network computing has revolutionized the field of machine learning. The systolicarray architecture is a widely used architecture for neural-network computing acceleration that was adopted by Google in its Tensor Processing Unit (TPU). To ensure the correct operation of the neural network, the reliability of the systolic-array architecture should be guaranteed. This paper proposes an efficient systolic-array redundancy architecture that is based on systolic-array partitioning and rearranging connections of the systolic-array elements. The proposed architecture allows both offline and online repair with an extended redundancy architecture and programmable fuses and can ensure reliability even in an online situation, for which the previous fault-tolerant schemes have not been considered.

Original language	English
Article number	338
Journal	Electronics (Switzerland)
Volume	9
Issue number	2
DOIs	https://doi.org/10.3390/electronics9020338
Publication status	Published - 2020 Feb

Bibliographical note

Funding Information:
validation, K.C., I.L., and H.L.; formal analysis, I.L.; investigation, K.C. and H.L.; resources, K.C.; data curation, K.C.; writing—original draft preparation, K.C. and I.L.; writing—review and editing, K.C. and S.K.; visualization, K.C. and I.L.; supervision, S.K.; project administration, S.K.; funding acquisition, S.K. All authors have read and agreed to the published version of the manuscript. agreed to the published version of the manuscript. Funding: This work was supported by Samsung Electronics Company, Ltd., Hwaseong, Korea. Funding: This work was supported by Samsung Electronics Company, Ltd., Hwaseong, Korea. Conflicts of Interest: The authors declare no conflict of interest. Conflicts of Interest: The authors declare no conflict of interest.

Publisher Copyright:
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.

All Science Journal Classification (ASJC) codes

Control and Systems Engineering
Signal Processing
Hardware and Architecture
Computer Networks and Communications
Electrical and Electronic Engineering

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abstract = "Neural-network computing has revolutionized the field of machine learning. The systolicarray architecture is a widely used architecture for neural-network computing acceleration that was adopted by Google in its Tensor Processing Unit (TPU). To ensure the correct operation of the neural network, the reliability of the systolic-array architecture should be guaranteed. This paper proposes an efficient systolic-array redundancy architecture that is based on systolic-array partitioning and rearranging connections of the systolic-array elements. The proposed architecture allows both offline and online repair with an extended redundancy architecture and programmable fuses and can ensure reliability even in an online situation, for which the previous fault-tolerant schemes have not been considered.",

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Efficient systolic-array redundancy architecture for offline/online repair

Abstract

Bibliographical note

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