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1. Training Your Sparse Neural Network Better with Any Mask

   Jaiswal, Ajay; Ma, Haoyu; Chen, Tianlong; Ding, Ying; Wang, Zhangyang (July 2022, International Conference on Machine Learning (ICML))

   Pruning large neural networks to create high-quality, independently trainable sparse masks, which can maintain similar performance to their dense counterparts, is very desirable due to the reduced space and time complexity. As research effort is focused on increasingly sophisticated pruning methods that leads to sparse subnetworks trainable from the scratch, we argue for an orthogonal, under-explored theme: improving training techniques for pruned sub-networks, i.e. sparse training. Apart from the popular belief that only the quality of sparse masks matters for sparse training, in this paper we demonstrate an alternative opportunity: one can carefully customize the sparse training techniques to deviate from the default dense network training protocols, consisting of introducing ``ghost" neurons and skip connections at the early stage of training, and strategically modifying the initialization as well as labels. Our new sparse training recipe is generally applicable to improving training from scratch with various sparse masks. By adopting our newly curated techniques, we demonstrate significant performance gains across various popular datasets (CIFAR-10, CIFAR-100, TinyImageNet), architectures (ResNet-18/32/104, Vgg16, MobileNet), and sparse mask options (lottery ticket, SNIP/GRASP, SynFlow, or even randomly pruning), compared to the default training protocols, especially at high sparsity levels. 

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2. Rare Gems: Finding Lottery Tickets at Initialization

   Sreenivasan, Kartik; Sohn, Jy-yong; Yang, Liu; Grinde, Matthew; Nagle, Alliot; Wang, Hongyi; Xing, Eric; Lee, Kangwook; Papailiopoulos, Dimitris (January 2022, Advances in neural information processing systems)

   Large neural networks can be pruned to a small fraction of their original size, with little loss in accuracy, by following a time-consuming "train, prune, re-train" approach. Frankle & Carbin conjecture that we can avoid this by training lottery tickets, i.e., special sparse subnetworks found at initialization, that can be trained to high accuracy. However, a subsequent line of work presents concrete evidence that current algorithms for finding trainable networks at initialization, fail simple baseline comparisons, e.g., against training random sparse subnetworks. Finding lottery tickets that train to better accuracy compared to simple baselines remains an open problem. In this work, we resolve this open problem by proposing Gem-Miner which finds lottery tickets at initialization that beat current baselines. Gem-Miner finds lottery tickets trainable to accuracy competitive or better than Iterative Magnitude Pruning (IMP), and does so up to 19x faster. 

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3. Optimal Eye Surgeon: Finding image priors through sparse generators at initialization

   Ghosh, Avrajit; Zhang, Xitong; Sun, Kenneth K; Qu, Qing; Ravishankar, Saiprasad; Wang, Rongrong (June 2024, International Conference on Machine Learning)

   We introduce Optimal Eye Surgeon (OES), a framework for pruning and training deep image generator networks. Typically, untrained deep convolutional networks, which include image sampling operations, serve as effective image priors (Ulyanov et al., 2018). However, they tend to overfit to noise in image restoration tasks due to being overparameterized. OES addresses this by adaptively pruning networks at random initialization to a level of underparameterization. This process effectively captures low-frequency image components even without training, by just masking. When trained to fit noisy images, these pruned subnetworks, which we term Sparse-DIP, resist overfitting to noise. This benefit arises from underparameterization and the regularization effect of masking, constraining them in the manifold of image priors. We demonstrate that subnetworks pruned through OES surpass other leading pruning methods, such as the Lottery Ticket Hypothesis, which is known to be suboptimal for image recovery tasks (Wu et al., 2023). Our extensive experiments demonstrate the transferability of OES-masks and the characteristics of sparse-subnetworks for image generation. 

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4. ESCALATE: Boosting the Efficiency of Sparse CNN Accelerator with Kernel Decomposition

   https://doi.org/10.1145/3466752.3480043  

   Li, Shiyu; Hanson, Edward; Qian, Xuehai; Li, Hai "Helen"; Chen, Yiran (October 2021, IEEE/ACM International Symposium on Microarchitecture)

   null (Ed.)

   The ever-growing parameter size and computation cost of Convolutional Neural Network (CNN) models hinder their deployment onto resource-constrained platforms. Network pruning techniques are proposed to remove the redundancy in CNN parameters and produce a sparse model. Sparse-aware accelerators are also proposed to reduce the computation cost and memory bandwidth requirements of inference by leveraging the model sparsity. The irregularity of sparse patterns, however, limits the efficiency of those designs. Researchers proposed to address this issue by creating a regular sparsity pattern through hardware-aware pruning algorithms. However, the pruning rate of these solutions is largely limited by the enforced sparsity patterns. This limitation motivates us to explore other compression methods beyond pruning. With two decoupled computation stages, we found that kernel decomposition could potentially take the processing of the sparse pattern off from the critical path of inference and achieve a high compression ratio without enforcing the sparse patterns. To exploit these advantages, we propose ESCALATE, an algorithm-hardware co-design approach based on kernel decomposition. At algorithm level, ESCALATE reorganizes the two computation stages of the decomposed convolution to enable a stream processing of the intermediate feature map. We proposed a hybrid quantization to exploit the different reuse frequency of each part of the decomposed weight. At architecture level, ESCALATE proposes a novel ‘Basis-First’ dataflow and its corresponding microarchitecture design to maximize the benefits brought by the decomposed convolution. 

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5. Is Pruning Compression?: Investigating Pruning Via Network Layer Similarity

   Blakeney, Cody; Yan, Yan; Zong, Ziliang (March 2020, IEEE Winter Conference on Applications of Computer Vision (WACV ’20))

   Unstructured neural network pruning is an effective technique that can significantly reduce theoretical model size, computation demand and energy consumption of large neural networks without compromising accuracy. However, a number of fundamental questions about pruning are not answered yet. For example, do the pruned neural networks contain the same representations as the original network? Is pruning a compression or evolution process? Does pruning only work on trained neural networks? What is the role and value of the uncovered sparsity structure? In this paper, we strive to answer these questions by analyzing three unstructured pruning methods (magnitude based pruning, post-pruning re-initialization, and random sparse initialization). We conduct extensive experiments using the Singular Vector Canonical Correlation Analysis (SVCCA) tool to study and contrast layer representations of pruned and original ResNet, VGG, and ConvNet models. We have several interesting observations: 1) Pruned neural network models evolve to substantially different representations while still maintaining similar accuracy. 2) Initialized sparse models can achieve reasonably good accuracy compared to well engineered pruning methods. 3) Sparsity structures discovered by pruning models are not inherently important or useful. 

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