@inproceedings{2e82fb4e63ea46bdb01bc31669c0b902,
title = "XST: A Crossbar Column-wise Sparse Training for Efficient Continual Learning",
abstract = "Leveraging the ReRAM crossbar-based In-Memory-Computing (IMC) to accelerate single task DNN inference has been widely studied. However, using the ReRAM crossbar for continual learning has not been explored yet. In this work, we propose XST, a novel crossbar column-wise sparse training framework for continual learning. XST significantly reduces the training cost and saves inference energy. More importantly, it is friendly to existing crossbar-based convolution engine with almost no hardware overhead. Compared with the state-of-the-art CPG method, the experiments show that XST's accuracy achieves 4.95 % higher accuracy. Furthermore, XST demonstrates 5.59 × training speedup and 1.5 × inference energy-saving.",
keywords = "Continual Learning, In-Memory-Computing, Sparse Learning",
author = "Fan Zhang and Li Yang and Jian Meng and Seo, {Jae Sun} and Yu Cao and Deliang Fan",
note = "Publisher Copyright: {\textcopyright} 2022 EDAA.; 2022 Design, Automation and Test in Europe Conference and Exhibition, DATE 2022 ; Conference date: 14-03-2022 Through 23-03-2022",
year = "2022",
doi = "10.23919/DATE54114.2022.9774660",
language = "English (US)",
series = "Proceedings of the 2022 Design, Automation and Test in Europe Conference and Exhibition, DATE 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "48--51",
editor = "Cristiana Bolchini and Ingrid Verbauwhede and Ioana Vatajelu",
booktitle = "Proceedings of the 2022 Design, Automation and Test in Europe Conference and Exhibition, DATE 2022",
}