Asteroid: Resource-Efficient Hybrid Pipeline Parallelism for Collaborative DNN Training on Heterogeneous Edge Devices-Reference-Cited by-同舟云学术

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

Published:2024-05-29 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 30th Annual International Conference on Mobile Computing and Networking
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Author:

Ye Shengyuan¹^ORCID,Zeng Liekang¹^ORCID,Chu Xiaowen²^ORCID,Xing Guoliang³^ORCID,Chen Xu¹^ORCID

Affiliation:

1. Sun Yat-sen University, Guangzhou, China

2. Data Science and Analytics Thrust, HKUST(GZ), Guangzhou, China

3. The Chinese University of Hong Kong, Hong Kong SAR, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3636534.3649363

Reference66 articles.

1. 2017. Jetson-TX2. https://developer.nvidia.com/embedded/jetson-tx2.

2. 2019. Jetson-Nano. https://developer.nvidia.com/embedded/jetson-nano-developer-kit.

3. 2019. Jetson-NX. https://developer.nvidia.com/blog/jetson-xavier-nx-the-worlds-smallest-ai-supercomputer.

4. 2019. PyTorch. https://github.com/pytorch/pytorch.

5. 2019. PyTorch DDP. https://pytorch.org/docs/stable/_modules/torch/nn/parallel/distributed.html.

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Pluto and Charon: A Time and Memory Efficient Collaborative Edge AI Framework for Personal LLMs Fine-tuning;Proceedings of the 53rd International Conference on Parallel Processing;2024-08-12

2. Implementation of Big AI Models for Wireless Networks with Collaborative Edge Computing;IEEE Wireless Communications;2024-06

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