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1. Hyb-Learn: A Framework for On-Device Self-Supervised Continual Learning with Hybrid RRAM/SRAM Memory

   https://doi.org/10.1145/3649329.3657389  

   Zhang, Fan; Yang, Li; Fan, Deliang (June 2024, ACM)

   While RRAM crossbar-based In-Memory Computing (IMC) has proven highly effective in accelerating Deep Neural Networks (DNNs) inference, RRAM-based on-device training is less explored due to its high energy consumption of weight re-programming and cells' low endurance problem. Besides, emerging trends indicate a need for on-device continual learning which sequentially acquires knowledge from multiple tasks to enhance user's experiences and eliminate data privacy concerns. However, learning on each new task leads to forgetting prior learned knowledge on prior tasks, which is known as catastrophic forgetting. To address these challenges, we are the first to propose a novel training framework, Hyb-Learn, for enabling on-device continual learning with a hybrid RRAM/SRAM IMC architecture design. Specifically, when training each new arriving task, our approach first partitions the model into two groups based on the proposed task-correlated PE-wise correlation to freeze or re-training, and correspondingly mapping to RRAM and SRAM, respectively. In practice, the RRAM stores frozen weights with strong task correlation to prior tasks to eliminate the high cost of weight reprogramming issue of RRAM, while the SRAM stores the remaining weights that will be updated. Furthermore, to maximize the freezing ratio for improving training efficiency while maintaining accuracy and mitigating catastrophic forgetting, we incorporate self-supervised learning algorithms that are initialized from a pre-trained model for training each new task. 

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2. Visual Analytics on Network Forgetting for Task‐Incremental Learning

   https://doi.org/10.1111/cgf.14842  

   Li, Ziwei; Xu, Jiayi; Chao, Wei‐Lun; Shen, Han‐Wei (June 2023, Computer Graphics Forum)

   Abstract Task‐incremental learning (Task‐IL) aims to enable an intelligent agent to continuously accumulate knowledge from new learning tasks without catastrophically forgetting what it has learned in the past. It has drawn increasing attention in recent years, with many algorithms being proposed to mitigate neural network forgetting. However, none of the existing strategies is able to completely eliminate the issues. Moreover, explaining and fully understanding what knowledge and how it is being forgotten during the incremental learning process still remains under‐explored. In this paper, we propose KnowledgeDrift, a visual analytics framework, to interpret the network forgetting with three objectives: (1) to identify when the network fails to memorize the past knowledge, (2) to visualize what information has been forgotten, and (3) to diagnose how knowledge attained in the new model interferes with the one learned in the past. Our analytical framework first identifies the occurrence of forgetting by tracking the task performance under the incremental learning process and then provides in‐depth inspections of drifted information via various levels of data granularity. KnowledgeDrift allows analysts and model developers to enhance their understanding of network forgetting and compare the performance of different incremental learning algorithms. Three case studies are conducted in the paper to further provide insights and guidance for users to effectively diagnose catastrophic forgetting over time. 

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3. Sleep-like unsupervised replay reduces catastrophic forgetting in artificial neural networks

   https://doi.org/10.1038/s41467-022-34938-7  

   Tadros, Timothy; Krishnan, Giri P.; Ramyaa, Ramyaa; Bazhenov, Maxim (December 2022, Nature Communications)

   Abstract Artificial neural networks are known to suffer from catastrophic forgetting: when learning multiple tasks sequentially, they perform well on the most recent task at the expense of previously learned tasks. In the brain, sleep is known to play an important role in incremental learning by replaying recent and old conflicting memory traces. Here we tested the hypothesis that implementing a sleep-like phase in artificial neural networks can protect old memories during new training and alleviate catastrophic forgetting. Sleep was implemented as off-line training with local unsupervised Hebbian plasticity rules and noisy input. In an incremental learning framework, sleep was able to recover old tasks that were otherwise forgotten. Previously learned memories were replayed spontaneously during sleep, forming unique representations for each class of inputs. Representational sparseness and neuronal activity corresponding to the old tasks increased while new task related activity decreased. The study suggests that spontaneous replay simulating sleep-like dynamics can alleviate catastrophic forgetting in artificial neural networks. 

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4. Sleep prevents catastrophic forgetting in spiking neural networks by forming a joint synaptic weight representation

   https://doi.org/10.1371/journal.pcbi.1010628  

   Golden, Ryan; Delanois, Jean Erik; Sanda, Pavel; Bazhenov, Maxim (November 2022, PLOS Computational Biology)

   Bush, Daniel (Ed.)

   Artificial neural networks overwrite previously learned tasks when trained sequentially, a phenomenon known as catastrophic forgetting. In contrast, the brain learns continuously, and typically learns best when new training is interleaved with periods of sleep for memory consolidation. Here we used spiking network to study mechanisms behind catastrophic forgetting and the role of sleep in preventing it. The network could be trained to learn a complex foraging task but exhibited catastrophic forgetting when trained sequentially on different tasks. In synaptic weight space, new task training moved the synaptic weight configuration away from the manifold representing old task leading to forgetting. Interleaving new task training with periods of off-line reactivation, mimicking biological sleep, mitigated catastrophic forgetting by constraining the network synaptic weight state to the previously learned manifold, while allowing the weight configuration to converge towards the intersection of the manifolds representing old and new tasks. The study reveals a possible strategy of synaptic weights dynamics the brain applies during sleep to prevent forgetting and optimize learning. 

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5. Incremental Learning via Rate Reduction

   Wu, Ziyang; Baek, Christina; You, Chong; Ma, Yi (June 2021, IEEE Computer Society Conference on Computer Vision and Pattern Recognition)

   null (Ed.)

   Current deep learning architectures suffer from catastrophic forgetting, a failure to retain knowledge of previously learned classes when incrementally trained on new classes. The fundamental roadblock faced by deep learning methods is that the models are optimized as “black boxes,” making it difficult to properly adjust the model parameters to preserve knowledge about previously seen data. To overcome the problem of catastrophic forgetting, we propose utilizing an alternative “white box” architecture derived from the principle of rate reduction, where each layer of the network is explicitly computed without back propagation. Under this paradigm, we demonstrate that, given a pretrained network and new data classes, our approach can provably construct a new network that emulates joint training with all past and new classes. Finally, our experiments show that our proposed learning algorithm observes significantly less decay in classification performance, outperforming state of the art methods on MNIST and CIFAR-10 by a large margin and justifying the use of “white box” algorithms for incremental learning even for sufficiently complex image data. 

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