research-article

Automated Deep Learning Model Partitioning for Heterogeneous Edge Devices

Authors:

Arijit Mukherjee,

Swarnava DeyAuthors Info & Claims

AIMLSystems '22: Proceedings of the Second International Conference on AI-ML Systems

Article No.: 19, Pages 1 - 8

https://doi.org/10.1145/3564121.3564796

Published: 16 May 2023 Publication History

Abstract

Deep Neural Networks (DNN) have made machine learning accessible to a wide set of practitioners working with field deployment of analytics algorithms over sensor data. Along with it, focus on data privacy, low latency inference, and sustainability has highlighted the need for efficient in-situ analytics close to sensors, at the edge of the network, which is challenging given the constrained nature of the edge platforms, including Common Off-the-Shelf (COTS) AI accelerators. Efficient DNN model partitioning across multiple edge nodes is a well-studied approach, but no definitive characterization exists as to why there is a performance improvement due to DNN model partitioning, and whether the benefits hold for currently used edge hardware & state-of-the-art DNN models. In this paper, we present a detailed study and analyses to address the above-mentioned shortcomings and propose a framework that automatically determines the best partitioning scheme and enhances system efficiency.

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Cited By

Qi HRen FWang LJiang PWan SDeng X(2024)Multi-Compression Scale DNN Inference Acceleration based on Cloud-Edge-End CollaborationACM Transactions on Embedded Computing Systems10.1145/363470423:1(1-25)Online publication date: 19-Jan-2024
https://dl.acm.org/doi/10.1145/3634704
Zhang ZZhang YBao WLi CYuan D(2024)Coarse-to-Fine: A hierarchical DNN inference framework for edge computingFuture Generation Computer Systems10.1016/j.future.2024.03.009157(180-192)Online publication date: Aug-2024
https://doi.org/10.1016/j.future.2024.03.009

Index Terms

Automated Deep Learning Model Partitioning for Heterogeneous Edge Devices
1. Computer systems organization
  1. Dependable and fault-tolerant systems and networks
    1. Redundancy
  2. Embedded and cyber-physical systems
    1. Embedded systems
    2. Robotics
2. Networks
  1. Network properties
    1. Network reliability

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AIMLSystems '22: Proceedings of the Second International Conference on AI-ML Systems

October 2022

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DOI:10.1145/3564121

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Published: 16 May 2023

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Cited By

Qi HRen FWang LJiang PWan SDeng X(2024)Multi-Compression Scale DNN Inference Acceleration based on Cloud-Edge-End CollaborationACM Transactions on Embedded Computing Systems10.1145/363470423:1(1-25)Online publication date: 19-Jan-2024
https://dl.acm.org/doi/10.1145/3634704
Zhang ZZhang YBao WLi CYuan D(2024)Coarse-to-Fine: A hierarchical DNN inference framework for edge computingFuture Generation Computer Systems10.1016/j.future.2024.03.009157(180-192)Online publication date: Aug-2024
https://doi.org/10.1016/j.future.2024.03.009

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