Enabling On-Tiny-Device Model Personalization via Gradient Condensing and Alternant Partial Update
On-device training enables the model to adapt to user-specific data by fine-tuning a pre-trained model locally. As embedded devices become ubiquitous, on-device training is increasingly essential since users can benefit from the personalized model without transmitting data and model parameters to th...
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| Published in: | 2025 62nd ACM/IEEE Design Automation Conference (DAC) pp. 1 - 7 |
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| Main Authors: | , , , , , , , , |
| Format: | Conference Proceeding |
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IEEE
22.06.2025
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| Abstract | On-device training enables the model to adapt to user-specific data by fine-tuning a pre-trained model locally. As embedded devices become ubiquitous, on-device training is increasingly essential since users can benefit from the personalized model without transmitting data and model parameters to the server. Despite significant efforts toward efficient training, ondevice training still faces a major challenge: The prohibitive cost of multi-layer backpropagation strains the limited resources of tiny devices. In this paper, we propose an algorithm-system cooptimization framework TinyMP that enables self-adaptive on-tiny-device model personalization. To mitigate backpropagation costs, we introduce Gradient Condensing to condense the gradient map structure, significantly reducing the computational complexity and memory consumption of backpropagation while preserving model performance. To further reduce computation overhead, we propose Alternant Partial Update, a mechanism that locally and alternatively selects essential parameters to update without requiring retraining or offline evolutionary search. Our framework is evaluated through extensive experiments using various CNN models (e.g., MobileNetV2, MCUNet) on embedded devices with minimal resources (e.g., OpenMV-H7 with less than 1MB SRAM and 2 MB Flash). Experimental results show that our framework achieves up to 2.4 \times speedup, 80.8% memory saving, and 30.3% accuracy improvement on downstream tasks, outperforming SOTA approaches. |
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| AbstractList | On-device training enables the model to adapt to user-specific data by fine-tuning a pre-trained model locally. As embedded devices become ubiquitous, on-device training is increasingly essential since users can benefit from the personalized model without transmitting data and model parameters to the server. Despite significant efforts toward efficient training, ondevice training still faces a major challenge: The prohibitive cost of multi-layer backpropagation strains the limited resources of tiny devices. In this paper, we propose an algorithm-system cooptimization framework TinyMP that enables self-adaptive on-tiny-device model personalization. To mitigate backpropagation costs, we introduce Gradient Condensing to condense the gradient map structure, significantly reducing the computational complexity and memory consumption of backpropagation while preserving model performance. To further reduce computation overhead, we propose Alternant Partial Update, a mechanism that locally and alternatively selects essential parameters to update without requiring retraining or offline evolutionary search. Our framework is evaluated through extensive experiments using various CNN models (e.g., MobileNetV2, MCUNet) on embedded devices with minimal resources (e.g., OpenMV-H7 with less than 1MB SRAM and 2 MB Flash). Experimental results show that our framework achieves up to 2.4 \times speedup, 80.8% memory saving, and 30.3% accuracy improvement on downstream tasks, outperforming SOTA approaches. |
| Author | Wang, Zirui Liu, Huiguo Shen, Zhaoyan Bao, Zeyu Zhao, Mengying Jia, Zhenge Shi, Yiyang Pang, Xin Duan, Yu |
| Author_xml | – sequence: 1 givenname: Zhenge surname: Jia fullname: Jia, Zhenge organization: Shandong University,School of and Computer Science and Technology,China – sequence: 2 givenname: Yiyang surname: Shi fullname: Shi, Yiyang organization: Shandong University,School of and Computer Science and Technology,China – sequence: 3 givenname: Zeyu surname: Bao fullname: Bao, Zeyu organization: Shandong University,School of and Computer Science and Technology,China – sequence: 4 givenname: Zirui surname: Wang fullname: Wang, Zirui organization: Shandong University,School of and Computer Science and Technology,China – sequence: 5 givenname: Xin surname: Pang fullname: Pang, Xin organization: Shandong University,School of and Computer Science and Technology,China – sequence: 6 givenname: Huiguo surname: Liu fullname: Liu, Huiguo organization: Shandong University,School of and Computer Science and Technology,China – sequence: 7 givenname: Yu surname: Duan fullname: Duan, Yu organization: Shandong University,School of and Computer Science and Technology,China – sequence: 8 givenname: Zhaoyan surname: Shen fullname: Shen, Zhaoyan email: shenzhaoyan@sdu.edu.cn organization: Shandong University,School of and Computer Science and Technology,China – sequence: 9 givenname: Mengying surname: Zhao fullname: Zhao, Mengying organization: Shandong University,School of and Computer Science and Technology,China |
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| Snippet | On-device training enables the model to adapt to user-specific data by fine-tuning a pre-trained model locally. As embedded devices become ubiquitous,... |
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| SubjectTerms | Accuracy Adaptation models Backpropagation Computational modeling Costs Data models Performance evaluation Servers Strain Training |
| Title | Enabling On-Tiny-Device Model Personalization via Gradient Condensing and Alternant Partial Update |
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