PockEngine: Sparse and Efficient Fine-tuning in a Pocket

PockEngine: Sparse and Efficient Fine-tuning in a Pocket

On-device learning and efficient fine-tuning enable continuous and privacy-preserving customization (e.g., locally fine-tuning large language models on personalized data). However, existing training frameworks are designed for cloud servers with powerful accelerators (e.g., GPUs, TPUs) and lack the...

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Bibliographic Details
Main Authors: Zhu, Ligeng, Hu, Lanxiang, Lin, Ji, Chen, Wei-Ming, Wang, Wei-Chen, Gan, Chuang, Han, Song
Other Authors: Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Format: Article
Language:English
Published: ACM|56th Annual IEEE/ACM International Symposium on Microarchitecture 2024
Online Access:https://hdl.handle.net/1721.1/153267

Internet

https://hdl.handle.net/1721.1/153267

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