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...
Main Authors: | Zhu, Ligeng, Hu, Lanxiang, Lin, Ji, Chen, Wei-Ming, Wang, Wei-Chen, Gan, Chuang, Han, Song |
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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
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Online Access: | https://hdl.handle.net/1721.1/153267 |
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