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Robustly Executing DNNs in IoT Systems Using Coded Distributed Computing

Authors:

Ramyad Hadidi,

Jiashen Cao,

Michael S. Ryoo,

Hyesoon KimAuthors Info & Claims

DAC '19: Proceedings of the 56th Annual Design Automation Conference 2019

Article No.: 234, Pages 1 - 2

https://doi.org/10.1145/3316781.3322474

Published: 02 June 2019 Publication History

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Abstract

Internet of Things (IoT) devices have access to an abundance of raw data for processing. With deep neural networks (DNNs), not only the demand for the computing power of IoT devices is increasing, but also privacy concerns are motivating the importance of close-to-edge computation. DNN execution by distributing its computation is common in IoT systems. However, managing unstable latencies in a network and intermittent failures are serious challenges. Our work provides robustness and close-to-zero recovery latency by adapting coded distributed computing (CDC). We analyze robust execution on a mesh of Raspberry Pis by studying four DNNs.

References

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Hadidi, R., et al. Collaborative execution of deep neural networks on internet of things device. arXiv preprint (2018).

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Hadidi, R., et al. Real-time image recognition using collaborative iot devices. In ReQuEST '18 co-located with ASPLOS'18 (2018), ReQuEST '18, ACM.

Digital Library

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Li, S., et al. A fundamental tradeoff between computation and communication in distributed computing. IEEE Trans. Inf. Theory 64, 1 (Jan 2018), 109--128.

Crossref

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Ryoo, M. S., et al. Extreme low resolution activity recognition with multi-siamese embedding learning. In AAAI'18 (Feb. 2018), IEEE.

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Xia JLiu TLing ZWang TFu XChen M(2022)PervasiveFL: Pervasive Federated Learning for Heterogeneous IoT SystemsIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2022.319749141:11(4100-4111)Online publication date: Nov-2022
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Information

Published In

DAC '19: Proceedings of the 56th Annual Design Automation Conference 2019

June 2019

1378 pages

ISBN:9781450367257

DOI:10.1145/3316781

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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SIGBED: ACM Special Interest Group on Embedded Systems

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 June 2019

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Research-article
Research
Refereed limited

Funding Sources

National Science Foundation

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DAC '19

Sponsor:

SIGDA

DAC '19: The 56th Annual Design Automation Conference 2019

June 2 - 6, 2019

NV, Las Vegas, USA

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

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DAC '25

Sponsor:
sigda

62nd ACM/IEEE Design Automation Conference

June 22 - 26, 2025

San Francisco , CA , USA

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Guo XJiang QShen YPimentel AStefanov T(2024)EASTER: Learning to Split Transformers at the Edge RobustlyIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2024.343899543:11(3626-3637)Online publication date: Nov-2024
https://doi.org/10.1109/TCAD.2024.3438995
Xia JLiu TLing ZWang TFu XChen M(2022)PervasiveFL: Pervasive Federated Learning for Heterogeneous IoT SystemsIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2022.319749141:11(4100-4111)Online publication date: Nov-2022
https://doi.org/10.1109/TCAD.2022.3197491
Du JDu YHuang DLu YLiao X(2022)Enhancing Distributed In-Situ CNN Inference in the Internet of ThingsIEEE Internet of Things Journal10.1109/JIOT.2022.31764089:17(15511-15524)Online publication date: 1-Sep-2022
https://doi.org/10.1109/JIOT.2022.3176408
Soni DKumar N(2022)Machine learning techniques in emerging cloud computing integrated paradigmsJournal of Network and Computer Applications10.1016/j.jnca.2022.103419205:COnline publication date: 1-Sep-2022
https://dl.acm.org/doi/10.1016/j.jnca.2022.103419
Murshed MMurphy CHou DKhan NAnanthanarayanan GHussain F(2021)Machine Learning at the Network Edge: A SurveyACM Computing Surveys10.1145/346902954:8(1-37)Online publication date: 4-Oct-2021
https://dl.acm.org/doi/10.1145/3469029
Song YLiu TWei TWang XTao ZChen M(2021)FDA$^3$: Federated Defense Against Adversarial Attacks for Cloud-Based IIoT ApplicationsIEEE Transactions on Industrial Informatics10.1109/TII.2020.300596917:11(7830-7838)Online publication date: Nov-2021
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Zhang XHu MXia JWei TChen MHu S(2021)Efficient Federated Learning for Cloud-Based AIoT ApplicationsIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2020.304666540:11(2211-2223)Online publication date: Nov-2021
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Ng JLim WLuong NXiong ZAsheralieva ANiyato DLeung CMiao C(2021)A Comprehensive Survey on Coded Distributed Computing: Fundamentals, Challenges, and Networking ApplicationsIEEE Communications Surveys & Tutorials10.1109/COMST.2021.309168423:3(1800-1837)Online publication date: Nov-2022
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Liu TSong YHu MXia JZhang JChen M(2020)An Ensemble Learning-Based Cooperative Defensive Architecture Against Adversarial AttacksJournal of Circuits, Systems and Computers10.1142/S021812662150025030:02(2150025)Online publication date: 30-Jul-2020
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Hadidi RCao JRyoo MKim H(2020)Toward Collaborative Inferencing of Deep Neural Networks on Internet-of-Things DevicesIEEE Internet of Things Journal10.1109/JIOT.2020.29720007:6(4950-4960)Online publication date: Jun-2020
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