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.
more »
« less
KSM: Fast Multiple Task Adaption via Kernel-wise Soft Mask Learning
Deep Neural Networks (DNN) could forget the knowledge about earlier tasks when learning new tasks, and this is known as catastrophic forgetting. To learn new task without forgetting, recently, the mask-based learning method (e.g. piggyback ) is proposed to address these issues by learning only a binary element-wise mask, while keeping the backbone model fixed. However, the binary mask has limited modeling capacity for new tasks. A more recent work proposes a compress-grow-based method (CPG) to achieve better accuracy for new tasks by partially training backbone model, but with order-higher training cost, which makes it infeasible to be deployed into popular state-of-the-art edge-/mobile-learning. The primary goal of this work is to simultaneously achieve fast and high-accuracy multi-task adaption in a continual learning setting. Thus motivated, we propose a new training method called Kernel-wise Soft Mask (KSM), which learns a kernel-wise hybrid binary and real-value soft mask for each task. Such a soft mask can be viewed as a superposition of a binary mask and a properly scaled real-value tensor, which offers a richer representation capability without low-level kernel support to meet the objective of low hardware overhead. We validate KSM on multiple benchmark datasets against recent state-of-the-art methods (e.g. Piggyback, Packnet, CPG, etc.), which shows good improvement in both accuracy and training cost.
more »
« less
- PAR ID:
- 10295497
- Date Published:
- Journal Name:
- IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021
- Page Range / eLocation ID:
- 13840 to 13848
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
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
-
Existing work in continual learning (CL) focuses on mitigating catastrophic forgetting, i.e., model performance deterioration on past tasks when learning a new task. However, the training efficiency of a CL system is under-investigated, which limits the real-world application of CL systems under resource-limited scenarios. In this work, we propose a novel framework called Sparse Continual Learning(SparCL), which is the first study that leverages sparsity to enable cost-effective continual learning on edge devices. SparCL achieves both training acceleration and accuracy preservation through the synergy of three aspects: weight sparsity, data efficiency, and gradient sparsity. Specifically, we propose task-aware dynamic masking (TDM) to learn a sparse network throughout the entire CL process, dynamic data removal (DDR) to remove less informative training data, and dynamic gradient masking (DGM) to sparsify the gradient updates. Each of them not only improves efficiency, but also further mitigates catastrophic forgetting. SparCL consistently improves the training efficiency of existing state-of-the-art (SOTA) CL methods by at most 23X less training FLOPs, and, surprisingly, further improves the SOTA accuracy by at most 1.7%. SparCL also outperforms competitive baselines obtained from adapting SOTA sparse training methods to the CL setting in both efficiency and accuracy. We also evaluate the effectiveness of SparCL on a real mobile phone, further indicating the practical potential of our method.more » « less
-
Incremental Task learning (ITL) is a category of continual learning that seeks to train a single network for multiple tasks (one after another), where training data for each task is only available during the training of that task. Neural networks tend to forget older tasks when they are trained for the newer tasks; this property is often known as catastrophic forgetting. To address this issue, ITL methods use episodic memory, parameter regularization, masking and pruning, or extensible network structures. In this paper, we propose a new incremental task learning framework based on low-rank factorization. In particular, we represent the network weights for each layer as a linear combination of several rank-1 matrices. To update the network for a new task, we learn a rank-1 (or low-rank) matrix and add that to the weights of every layer. We also introduce an additional selector vector that assigns different weights to the low-rank matrices learned for the previous tasks. We show that our approach performs better than the current state-of-the-art methods in terms of accuracy and forgetting. Our method also offers better memory efficiency compared to episodic memory- and mask-based approaches. Our code will be available at https://github.com/CSIPlab/task-increment-rank-update.gitmore » « less
-
Recently, ReRAM crossbar-based deep neural network (DNN) accelerator has been widely investigated. However, most prior works focus on single-task inference due to the high energy consumption of weight reprogramming and ReRAM cells’ low endurance issue. Adapting the ReRAM crossbar-based DNN accelerator for multiple tasks has not been fully explored. In this study, we propose XMA 2 , a novel crossbar-aware learning method with a 2-tier masking technique to efficiently adapt a DNN backbone model deployed in the ReRAM crossbar for new task learning. During the XMA 2 -based multi-task adaption (MTA), the tier-1 ReRAM crossbar-based processing-element- (PE-) wise mask is first learned to identify the most critical PEs to be reprogrammed for essential new features of the new task. Subsequently, the tier-2 crossbar column-wise mask is applied within the rest of the weight-frozen PEs to learn a hardware-friendly and column-wise scaling factor for new task learning without modifying the weight values. With such crossbar-aware design innovations, we could implement the required masking operation in an existing crossbar-based convolution engine with minimal hardware/memory overhead to adapt to a new task. The extensive experimental results show that compared with other state-of-the-art multiple-task adaption methods, XMA 2 achieves the highest accuracy on all popular multi-task learning datasets.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/CVPR46437.2021.01363
