skip to main content

Attention:

The NSF Public Access Repository (NSF-PAR) system and access will be unavailable from 11:00 PM ET on Friday, April 12 until 2:00 AM ET on Saturday, April 13 due to maintenance. We apologize for the inconvenience.


Search for: All records

Creators/Authors contains: "Cao, Yu Kevin"

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. 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. 
    more » « less