With the growing scale of deep neural networks and datasets, training has become increasingly expensive. Layer freezing reduces this cost by stopping updates to selected layers, but frozen layers still require forward propagation to generate activations for later layers. Caching these activations as a surrogate dataset can eliminate this redundant computation, but it faces two key challenges: effectively augmenting cached features and reducing the storage overhead of high-dimensional activations. This paper provides the first systematic study of these challenges and proposes practical solutions. We introduce Similarity-Aware Channel Augmentation to preserve accuracy by caching transformation-sensitive channels with limited overhead. We further incorporate lossy compression and design a progressive compression strategy that exploits the higher compressibility of deeper-layer activations. Our method reduces computation cost, memory usage, and training time while maintaining accuracy. Experiments on NVIDIA Orin Edge GPU further demonstrate training acceleration and significant power savings, highlighting its practicality for resource-constrained training.

In Computer Vision (CV), the deployment of Convolutional Neural Networks (CNNs) is often hindered by their substantial computational requirements and large labeled datasets. Self-supervised learning (SSL) serves as an effective approach to reducing the reliance on labeled data with the option of augmentation methods to infer and train CNNs. Excluding irrelevant features accelerates learning and improves optimization. We propose a Field-Programmable Gate Array (FPGA)-based hardware accelerator architecture tailored for SSL framework, leveraging its parallelism and reconfigurability to expedite block matching, optimize sparse convolutions, and manage data reuse, significantly improving resource and energy efficiency. The implementation and evaluation of our work on Xilinx ZCU102 FPGA working at 200 MHz confirm that the similarity finding part's FPGA accelerations with a low hardware overhead generates a latency of 0.0106 seconds, surpassing GPU and CPU, and in the sparse CNN's FPGA acceleration part, with the processing of VGG16 and ResNet50, compared with the related FPGA-based works, our design claims a maximum of 3.08× throughput improvement and 1.5× in energy efficiency.

Self-supervised learning (SSL) offers a compelling solution to the challenge of extensive labeled data requirements in traditional supervised learning. With the proven success of Vision Transformers (ViTs) in supervised tasks, there is increasing interest in adapting them for SSL frameworks. However, the high computational demands of SSL pose substantial challenges, particularly on resource-limited platforms like edge devices, despite its ability to achieve high accuracy without labeled data. Recent studies in supervised learning have shown that token pruning can reduce training costs by removing less informative tokens without compromising accuracy. However, SSL’s dual-branch encoders make traditional single-branch pruning strategies less effective, as they fail to account for the critical cross-branch similarity information, leading to reduced accuracy in SSL. To this end, we introduce SimPrune, a novel token pruning strategy designed for ViTs in SSL. SimPrune leverages cross-branch similarity information to efficiently prune tokens, retaining essential semantic information across dual branches. Additionally, we incorporate a difficulty-aware pruning strategy to further enhance SimPrune's effectiveness. Experimental results show that our proposed approach effectively reduces training computation while maintaining accuracy. Specifically, our approach offers 24\% savings in training costs compared to SSL baseline, without sacrificing accuracy.

Deep Neural Networks (DNNs), essential for diverse applications such as visual recognition and eldercare, often require a large amount of labeled data for training, making widespread deployment of DNNs a challenging task. Self-supervised learning (SSL) emerges as a promising approach, which leverages inherent patterns within data through diverse augmentations to train models without explicit labels. However, while SSL has shown notable advancements in accuracy, its high computation costs remain a daunting impediment, particularly for resource-constrained platforms. To address this problem, we introduce SimWnW, a similarity-based efficient self-supervised learning framework. By strategically removing less important regions in augmented images and feature maps, SimWnW not only reduces computation costs but also eliminates irrelevant features that might slow down the learning process, thereby accelerating model convergence. The experimental results show that SimWnW effectively reduces the amount of computation costs in self-supervised model training without compromising accuracy. Specifically, SimWnW yields up to 54\% and 51\% computation savings in training from scratch and transfer learning tasks, respectively.

Recently, sparse training has emerged as a promising paradigm for efficient deep learning on edge devices. The current research mainly devotes the efforts to reducing training costs by further increasing model sparsity. However, increasing sparsity is not always ideal since it will inevitably introduce severe accuracy degradation at an extremely high sparsity level. This paper intends to explore other possible directions to effectively and efficiently reduce sparse training costs while preserving accuracy. To this end, we investigate two techniques, namely, layer freezing and data sieving. First, the layer freezing approach has shown its success in dense model training and fine-tuning, yet it has never been adopted in the sparse training domain. Nevertheless, the unique characteristics of sparse training may hinder the incorporation of layer freezing techniques. Therefore, we analyze the feasibility and potentiality of using the layer freezing technique in sparse training and find it has the potential to save considerable training costs. Second, we propose a data sieving method for dataset-efficient training, which further reduces training costs by ensuring only a partial dataset is used throughout the entire training process. We show that both techniques can be well incorporated into the sparse training algorithm to form a generic framework, which we dub SpFDE. Our extensive experiments demonstrate that SpFDE can significantly reduce training costs while preserving accuracy from three dimensions: weight sparsity, layer freezing, and dataset sieving. Our code and models will be released.