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Characterizing the Execution of Deep Neural Networks on Collaborative Robots and Edge Devices

Authors:

Matthew L. Merck,

Arthur Siqueira,

Abhijeet Saraha,

Hyesoon KimAuthors Info & Claims

PEARC '19: Practice and Experience in Advanced Research Computing 2019: Rise of the Machines (learning)

Article No.: 65, Pages 1 - 6

https://doi.org/10.1145/3332186.3333049

Published: 28 July 2019 Publication History

Abstract

Edge devices and robots have access to an abundance of raw data that needs to be processed on the edge. Deep neural networks (DNNs) can help these devices understand and learn from this complex data; however, executing DNNs while achieving high performance is a challenge for edge devices. This is because of the high computational demands of DNN execution in real-time. This paper describes and implements a method to enable edge devices to execute DNNs collaboratively. This is possible and useful because in many environments, several on-edge devices are already integrated in their surroundings, but are usually idle and can provide additional computing power to a distributed system. We implement this method on two iRobots, each of which has been equipped with a Raspberry Pi 3. Then, we characterize the execution performance, communication latency, energy consumption, and thermal behavior of our system while it is executing AlexNet.

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Cited By

Siwach GLi C(2024)Unveiling the Potential of Natural Language Processing in Collaborative Robots (Cobots): A Comprehensive Survey2024 IEEE International Conference on Consumer Electronics (ICCE)10.1109/ICCE59016.2024.10444393(1-6)Online publication date: 6-Jan-2024
https://doi.org/10.1109/ICCE59016.2024.10444393
Liu WYu BGan YLiu QTang JLiu SZhu Y(2021)Archytas: A Framework for Synthesizing and Dynamically Optimizing Accelerators for Robotic LocalizationMICRO-54: 54th Annual IEEE/ACM International Symposium on Microarchitecture10.1145/3466752.3480077(479-493)Online publication date: 18-Oct-2021
https://dl.acm.org/doi/10.1145/3466752.3480077
Gan YBo YTian BXu LHu WLiu SLiu QZhang YTang JZhu Y(2021)Eudoxus: Characterizing and Accelerating Localization in Autonomous Machines Industry Track Paper2021 IEEE International Symposium on High-Performance Computer Architecture (HPCA)10.1109/HPCA51647.2021.00074(827-840)Online publication date: Feb-2021
https://doi.org/10.1109/HPCA51647.2021.00074
Show More Cited By

Index Terms

Characterizing the Execution of Deep Neural Networks on Collaborative Robots and Edge Devices
1. Computer systems organization
  1. Embedded and cyber-physical systems
2. Computing methodologies
  1. Distributed computing methodologies
  2. Machine learning

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Published In

cover image ACM Other conferences

PEARC '19: Practice and Experience in Advanced Research Computing 2019: Rise of the Machines (learning)

July 2019

775 pages

ISBN:9781450372275

DOI:10.1145/3332186

General Chair:
Tom Furlani
Roswell Park Comprehensive Cancer Center

Copyright © 2019 ACM.

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

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Publication History

Published: 28 July 2019

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National Science Foundation

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PEARC '19

PEARC '19: Practice and Experience in Advanced Research Computing

July 28 - August 1, 2019

IL, Chicago, USA

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Overall Acceptance Rate 133 of 202 submissions, 66%

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Cited By

Siwach GLi C(2024)Unveiling the Potential of Natural Language Processing in Collaborative Robots (Cobots): A Comprehensive Survey2024 IEEE International Conference on Consumer Electronics (ICCE)10.1109/ICCE59016.2024.10444393(1-6)Online publication date: 6-Jan-2024
https://doi.org/10.1109/ICCE59016.2024.10444393
Liu WYu BGan YLiu QTang JLiu SZhu Y(2021)Archytas: A Framework for Synthesizing and Dynamically Optimizing Accelerators for Robotic LocalizationMICRO-54: 54th Annual IEEE/ACM International Symposium on Microarchitecture10.1145/3466752.3480077(479-493)Online publication date: 18-Oct-2021
https://dl.acm.org/doi/10.1145/3466752.3480077
Gan YBo YTian BXu LHu WLiu SLiu QZhang YTang JZhu Y(2021)Eudoxus: Characterizing and Accelerating Localization in Autonomous Machines Industry Track Paper2021 IEEE International Symposium on High-Performance Computer Architecture (HPCA)10.1109/HPCA51647.2021.00074(827-840)Online publication date: Feb-2021
https://doi.org/10.1109/HPCA51647.2021.00074
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
https://doi.org/10.1109/JIOT.2020.2972000
Abbas MNarayan JBanerjee SDwivedy S(2020)AlexNet based Real-Time Detection and Segregation of Household Objects using Scorbot2020 4th International Conference on Computational Intelligence and Networks (CINE)10.1109/CINE48825.2020.234392(1-6)Online publication date: Feb-2020
https://doi.org/10.1109/CINE48825.2020.234392
Hadidi RCao JXie YAsgari BKrishna TKim H(2019)Characterizing the Deployment of Deep Neural Networks on Commercial Edge Devices2019 IEEE International Symposium on Workload Characterization (IISWC)10.1109/IISWC47752.2019.9041955(35-48)Online publication date: Nov-2019
https://doi.org/10.1109/IISWC47752.2019.9041955

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