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ReRAM crossbar array as a high-parallel fast and energy-efficient structure attracts much attention, especially on the acceleration of Deep Neural Network (DNN) inference on one specific task. However, due to the high energy consumption of weight re-programming and the ReRAM cells’ low endurance problem, adapting the crossbar array for multiple tasks has not been well explored. In this paper, we propose XMA, a novel crossbar-aware shift-based mask learning method for multiple task adaption in the ReRAM crossbar DNN accelerator for the first time. XMA leverages the popular mask-based learning algorithm’s benefit to mitigate catastrophic forgetting and learn a task-specific, crossbar column-wise, and shift-based multi-level mask, rather than the most commonly used elementwise binary mask, for each new task based on a frozen backbone model. With our crossbar-aware design innovation, the required masking operation to adapt for a new task could be implemented in an existing crossbar-based convolution engine with minimal hardware/memory overhead and, more importantly, no need for power-hungry cell re-programming, unlike prior works. The extensive experimental results show that, compared with state-of-the art multiple task adaption Piggyback method [1], XMA achieves 3.19% higher accuracy on average, while saving 96.6% memory overhead. Moreover, by eliminating cell re-programming, XMA achieves ∼4.3× higher energy efficiency than Piggyback.
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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.
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- PAR ID:
- 10348293
- Date Published:
- Journal Name:
- 2022 27th Asia and South Pacific Design Automation Conference (ASP-DAC)
- Page Range / eLocation ID:
- 610 to 615
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ASP-DAC52403.2022.9712508
