@inproceedings{a50a96bfa7084d78a14a1c44c0db0259,
title = "Dynamic Gradient Sparse Update for Edge Training",
abstract = "Training on edge devices enables personalized model fine-tuning to enhance real-world performance and maintain data privacy. However, the gradient computation for backpropagation in the training requires significant memory buffers to store intermediate features and compute losses. This is unacceptable for memory-constrained edge devices such as microcontrollers. To tackle this issue, we propose a training acceleration method using dynamic gradient sparse updates. This method updates the important channels and layers only and skips gradient computation for the less important channels and layers to reduce memory usage for each update iteration. In addition, the channel selection is dynamic for different iterations to traverse most of the parameters in the update layers along the time dimension for better performance. The experimental result shows that the proposed method enables an ImageNet pre-trained MobileNetV2 trained on CIFAR-10 to achieve an accuracy of 85.77% while updating only 2% of convolution weights within 256KB on-chip memory. This results in a remarkable 98% reduction in feature memory usage compared to dense model training.",
keywords = "dynamic training, memory-efficient training",
author = "Li, {I. Hsuan} and Chang, {Tian Sheuan}",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 2024 IEEE International Symposium on Circuits and Systems, ISCAS 2024 ; Conference date: 19-05-2024 Through 22-05-2024",
year = "2024",
doi = "10.1109/ISCAS58744.2024.10558072",
language = "English",
series = "Proceedings - IEEE International Symposium on Circuits and Systems",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "ISCAS 2024 - IEEE International Symposium on Circuits and Systems",
address = "United States",
}