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This content will become publicly available on January 17, 2023

Title: XBM: A Crossbar Column-wise Binary Mask Learning Method for Efficient Multiple Task Adaption
Recently, utilizing ReRAM crossbar array to accelerate DNN inference on single task has been widely studied. However, using the crossbar array for multiple task adaption has not been well explored. In this paper, for the first time, we propose XBM, a novel crossbar column-wise binary mask learning method for multiple task adaption in ReRAM crossbar DNN accelerator. XBM leverages the mask-based learning algorithm's benefit to avoid catastrophic forgetting to learn a task-specific mask for each new task. With our hardware-aware design innovation, the required masking operation to adapt for a new task could be easily implemented in existing crossbar based convolution engine with minimal hardware/ memory overhead and, more importantly, no need of power hungry cell re-programming, unlike prior works. The extensive experimental results show that compared with state-of-the-art multiple task adaption methods, XBM keeps the similar accuracy on new tasks while only requires 1.4% mask memory size compared with popular piggyback. Moreover, the elimination of cell re-programming or tuning saves up to 40% energy during new task adaption.
Authors:
; ; ; ; ;
Award ID(s):
2003749 1931871
Publication Date:
NSF-PAR ID:
10348293
Journal Name:
2022 27th Asia and South Pacific Design Automation Conference (ASP-DAC)
Page Range or eLocation-ID:
610 to 615
Sponsoring Org:
National Science Foundation
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