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This content will become publicly available on March 14, 2023

Title: XST: A Crossbar Column-wise Sparse Training for Efficient Continual Learning
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.
Authors:
; ; ; ; ;
Award ID(s):
2003749 1931871 2144751
Publication Date:
NSF-PAR ID:
10348290
Journal Name:
2022 Design, Automation & Test in Europe Conference & Exhibition (DATE)
Page Range or eLocation-ID:
48 to 51
Sponsoring Org:
National Science Foundation
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