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Asteroid: Resource-Efficient Hybrid Pipeline Parallelism for Collaborative DNN Training on Heterogeneous Edge Devices

Authors:

Xu ChenAuthors Info & Claims

ACM MobiCom '24: Proceedings of the 30th Annual International Conference on Mobile Computing and Networking

Pages 312 - 326

https://doi.org/10.1145/3636534.3649363

Published: 29 May 2024 Publication History

Abstract

On-device Deep Neural Network (DNN) training has been recognized as crucial for privacy-preserving machine learning at the edge. However, the intensive training workload and limited onboard computing resources pose significant challenges to the availability and efficiency of model training. While existing works address these challenges through native resource management optimization, we instead leverage our observation that edge environments usually comprise a rich set of accompanying trusted edge devices with idle resources beyond a single terminal. We propose Asteroid, a distributed edge training system that breaks the resource walls across heterogeneous edge devices for efficient model training acceleration. Asteroid adopts a hybrid pipeline parallelism to orchestrate distributed training, along with a judicious parallelism planning for maximizing throughput under certain resource constraints. Furthermore, a fault-tolerant yet lightweight pipeline replay mechanism is developed to tame the device-level dynamics for training robustness and performance stability. We implement Asteroid on heterogeneous edge devices with both vision and language models, demonstrating up to 12.2× faster training than conventional parallelism methods and 2.1× faster than state-of-the-art hybrid parallelism methods through evaluations. Furthermore, Asteroid can recover training pipeline 14× faster than baseline methods while preserving comparable throughput despite unexpected device exiting and failure.

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Cited By

Ouyang BYe SZeng LQian TLi JChen X(2024)Pluto and Charon: A Time and Memory Efficient Collaborative Edge AI Framework for Personal LLMs Fine-tuningProceedings of the 53rd International Conference on Parallel Processing10.1145/3673038.3673043(762-771)Online publication date: 12-Aug-2024
https://dl.acm.org/doi/10.1145/3673038.3673043
Zeng LYe SChen XYang Y(2024)Implementation of Big AI Models for Wireless Networks with Collaborative Edge ComputingIEEE Wireless Communications10.1109/MWC.004.230047931:3(50-58)Online publication date: Jun-2024
https://doi.org/10.1109/MWC.004.2300479
Ma MGong CZeng LYang YWu L(2024)FlocOff: Data Heterogeneity Resilient Federated Learning With Communication-Efficient Edge OffloadingIEEE Journal on Selected Areas in Communications10.1109/JSAC.2024.343152642:11(3262-3277)Online publication date: Nov-2024
https://doi.org/10.1109/JSAC.2024.3431526

Index Terms

Asteroid: Resource-Efficient Hybrid Pipeline Parallelism for Collaborative DNN Training on Heterogeneous Edge Devices
1. Computing methodologies
  1. Artificial intelligence
    1. Distributed artificial intelligence
2. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Ubiquitous and mobile computing systems and tools

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cover image ACM Conferences

ACM MobiCom '24: Proceedings of the 30th Annual International Conference on Mobile Computing and Networking

December 2024

2476 pages

ISBN:9798400704895

DOI:10.1145/3636534

Chair:
Weisong Shi,
Program Chairs:
Deepak Ganesan,
Nicholas Lane

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Published: 29 May 2024

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ACM MobiCom '24: 30th Annual International Conference on Mobile Computing and Networking

November 18 - 22, 2024

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Cited By

Ouyang BYe SZeng LQian TLi JChen X(2024)Pluto and Charon: A Time and Memory Efficient Collaborative Edge AI Framework for Personal LLMs Fine-tuningProceedings of the 53rd International Conference on Parallel Processing10.1145/3673038.3673043(762-771)Online publication date: 12-Aug-2024
https://dl.acm.org/doi/10.1145/3673038.3673043
Zeng LYe SChen XYang Y(2024)Implementation of Big AI Models for Wireless Networks with Collaborative Edge ComputingIEEE Wireless Communications10.1109/MWC.004.230047931:3(50-58)Online publication date: Jun-2024
https://doi.org/10.1109/MWC.004.2300479
Ma MGong CZeng LYang YWu L(2024)FlocOff: Data Heterogeneity Resilient Federated Learning With Communication-Efficient Edge OffloadingIEEE Journal on Selected Areas in Communications10.1109/JSAC.2024.343152642:11(3262-3277)Online publication date: Nov-2024
https://doi.org/10.1109/JSAC.2024.3431526

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