Year: 2024
Type: conference-paper
Abstract: On-device training Neural Networks (NNs) has been a crucial catalyst towards privacy-preserving and personalized mobile intelligence. Recently, a novel training paradigm, namely Parameter-Efficient Training (PET), is attracting attention in both the machine learning and system community. In our preliminary measurements, we find PET well-suited for on-device scenarios; yet, its parameter efficiency does not translate coequal to time efficiency on resource-constrained devices, as the training time is dominated by the frozen layers.
Authors: Wangsong Yin, Daliang Xu, Gang Huang, Ying Zhang, Shiyun Wei, Mengwei Xu, Xuanzhe Liu
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
Award: G8414340155
OpenAlex ID: W4404034656