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Aperiodic Local SGD: Beyond Local SGD 22

Aperiodic Local SGD: Beyond Local SGD

引用

Proceedings of the 51st International conference on parallel processing

作者： Hao Zhang Tingting Wu Siyao Cheng Jie Liu Harbin Institute of Technology China Harbin Institute of Technology (Shenzhen) China

ISBN: (纸本)9781450397339

Variations of stochastic gradient decedent (SGD) methods are at the core of training deep neural network models. However, in distributed deep learning, where multiple computing devices and data segments are employed in the training process, the performance of SGD can be significantly limited by the overhead of gradient communication. Local SGD methods are designed to overcome this bottleneck by averaging individual gradients trained over parallel workers after multiple local iterations. Currently, both for theoretical analyses and for practical applications, most studies employ periodic synchronization scheme by default, while few of them focus on the aperiodic schemes to obtain better performance models with limited computation and communication overhead. In this paper, we investigate local SGD with an arbitrary synchronization scheme to answer two questions: (1) Is the periodic synchronization scheme best? (2) If not, what is the optimal one? First, for any synchronization scheme, we derive the performance boundary with fixed overhead, and formulate the performance optimization under given computation and communication constraints. Then we find a succinct property of the optimal scheme that the local iteration number decreases as training continues, which indicates the periodic one is suboptimal. Furthermore, with some reasonable approximations, we obtain an explicit form of the optimal scheme and propose Aperiodic Local SGD (ALSGD) as an improved substitute for local SGD without any overhead increment. Our experiments also confirm that with the same computation and communication overhead, ALSGD outperforms local SGD in performance, especially for heterogeneous data.

关键词： computation and communication cost federated learning distributed learning local SGD data heterogeneity

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Identification of Mangrove Leaf Disease in Deep Convolutional Neural Network Approach

Identification of Mangrove Leaf Disease in Deep Convolutiona...

引用

Control & Automation, Electronics, Robotics, Internet of Things, and Artificial Intelligence (CERIA), IEEE International conference on

作者： Romi Fadillah Rahmat Opim Salim Sitompul Maya Silvi Lydia Fahmi Fahmi Raska Almashura Fahrurrozi Lubis Department of Information Technology Universitas Sumatera Utara Medan Indonesia Department of Electrical Engineering Universitas Sumatera Utara Medan Indonesia

ISBN: (数字)9798331511166

ISBN: (纸本)9798331511173

Mangrove forests in Indonesia are distributed over many coastal regions, exhibiting significant biodiversity and diverse structural forms. Mangroves serve as a mitigating factor against wave action and intense wind energy resulting from adverse weather conditions. Another purpose of mangroves is to provide as a food source for various marine species. Mangroves consist of many principal components, including roots, stems, leaves, and blooms. For optimal growth, some portions of mangrove plants must be safeguarded from several diseases, whether induced by parasites/pests or by environmental conditions. The leaves of the mangrove are the most vulnerable to disease; yet, pests visible on the leaves are typically inadequately managed, resulting in the plant being immediately removed when all its leaves are infested. This study uses a Deep Convolutional Neural Network to recognize these pests effectively. The image processing methods used in this study include smoothing and sharpening. The study findings indicate that the used approach effectively identifies mangrove leaf diseases, with an accuracy of 99.2%.

关键词： Accuracy Smoothing methods Wind energy Noise Sea measurements Real-time systems Mobile handsets Convolutional neural networks Robots Diseases

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parallel Vertex Color Update on Large Dynamic Networks

Parallel Vertex Color Update on Large Dynamic Networks

引用

International conference on High Performance Computing

作者： Arindam Khanda Sanjukta Bhowmick Xin Liang Sajal K. Das Missouri University of Science and Technology Rolla USA University of North Texas Denton USA

We present the first GPU-based parallel algorithm to efficiently update vertex coloring on large dynamic networks. For single GPU, we introduce the concept of loosely maintained vertex color update that reduces computation and memory requirements. For multiple GPUs, in distributed environments, we propose priority-based ordering of vertices to reduce the communication time. We prove the correctness of our algorithms and experimentally demonstrate that for graphs of over 16 million vertices and over 134 million edges on a single GPU, our dynamic algorithm is as much as 20x faster than state-of-the-art algorithm on static graphs. For larger graphs with over 130 million vertices and over 260 million edges, our distributed implementation with 8 GPUs produces updated color assignments within 160 milliseconds. In all cases, the proposed parallel algorithms produce comparable or fewer colors than state-of-the-art algorithms.

关键词： image color analysis Heuristic algorithms High performance computing Memory management Graphics processing units parallel algorithms Kernel

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Hybrid Token Selector based Accelerator for ViTs

Hybrid Token Selector based Accelerator for ViTs

引用

Design, Automation and Test in Europe conference and Exhibition

作者： Akshansh Yadav Anadi Goyal Palash Das Department of CSE Indian Institute of Technology Jodhpur Jodhpur India

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

Vision Transformers (ViTs) have shown great success in computer vision but suffer from high computational complexity due to the quadratic growth in the number of tokens processed. Token selection/pruning has emerged as a promising solution; however, early methods introduce significant overhead and complexity. Applying a token selector in the early layers of a ViT can yield substantial computational savings (GFLOPs) compared to using it in later layers. However, this approach often leads to significant accuracy loss, particularly with the popular Attention-based Token Selection (ATS) technique. To address these issues, we propose a hybrid token selection (HTS) strategy that integrates our Keypoint-based Token Selection (KTS) with the existing ATS method. KTS dynamically selects important tokens based on image content in the early layers, while ATS refines token pruning in the later layers. This hybrid approach reduces computational costs while maintaining accuracy. Additionally, we design custom hardware modules to accelerate the execution of the proposed methods and the ViT backbone. The proposed HTS delivers a 35.85% reduction in execution time relative to the baseline without any token selection. Furthermore, our results demonstrate that HTS achieves up to a 0.39% increase in accuracy and offers up to 6.05% savings in GFLOPs compared to existing method.

关键词： High-temperature superconductors Computer vision Accuracy Life estimation parallel processing Transformers Throughput Hardware Computational efficiency Resource management

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Quality Assessment of Screen Content images Based on Multi-Pathway Convolutional Neural Network

Quality Assessment of Screen Content Images Based on Multi-P...

引用

IEEE Visual Communications and image processing (VCIP)

作者： Mengyao Li Sumei Li School of Electrical and Information Engineering Tianjin University Tianjin China

ISBN: (纸本)9781665475938

In this paper, considering the retinal structure of human eye, and the composition characteristics of screen content images (SCIs), a multi-pathway convolutional neural network (CNN) with picture-text competition is proposed for SCIs quality assessment. According to the visual mechanism of human retina, we design a retinal structure simulation module, which uses multiple parallel convolution pathways to simulate the parallel transmission of visual signals by bipolar cells and uses a multi-pathway feature fusion (MPFF) module to allocate the weight for each channel to simulate horizontal cells' regulation of the information transmission. In addition, we design an adaptive feature extraction and competition module (AFEC) to directly extract the features of textural and pictorial regions and distribute the weight. Furthermore, the attention module combined with deformable convolution and channel attention can accurately extract image edge features and reduce redundancy of information. Experimental results show that the proposed method is superior to the mainstream methods.

关键词： Visualization Convolution Visual communication image edge detection Feature extraction Retina Regulation

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Dunhuang murals image inpainting method based on fusion classification pre-training model

Dunhuang murals image inpainting method based on fusion clas...

引用

Culture-Oriented Science and Technology (CoST), International conference on

作者： Hui Ren Chengya Zhang Ke Sun Chenyu Ye School of Information and Communication Engineering Communication University of China Beijing China Key Laboratory of Audiovisual Technology and Intelligent Control System Ministry of Culture and Tourism Beijing China Beijing Key Laboratory of Modern Performing Arts Technology Beijing China School of Engineering and Applied Science University of Pennsylvania PA USA

ISBN: (数字)9798350380347

ISBN: (纸本)9798350380354

Murals are an important part of China’s cultural heritage. Because of their age, Dunhuang murals have suffered from discoloration, fading and damage. With the development of computer image restoration technology, digital image processing provides strong support for mural restoration. Although there are many research results in the field of ancient mural images, there is no pre-trained model based on ancient mural images available for migration. There is a method for Dunhuang murals image restoration based on Generative Adversarial Network (GAN) with improved ResNet50 structure as an encoder in the generator. This research result proposes a generative adversarial network model with a parallel double convolutional extraction of deep features for the generator, combined with a ternary heterogeneous joint discriminator. On this basis, this paper proposes a binary classification task to distinguish ancient mural images from modern natural style images, so that the model pre-learns features such as color, texture, and structure of mural images, which are inputted into the restoration network as a priori information, in order to achieve the effect of reducing the probability of incomplete restoration of the model, and better generalization ability. The experimental results show that the method proposed in this paper has an overall improvement in the evaluation indexes such as Peak Signal to Noise Ratio(PSNR) and Structural Similarities(SSIM) when compared with other methods, and the restored images are more in line with human subjective vision, which proves the validity of the method.

关键词： Training Uncertainty Noise Maintenance engineering Generative adversarial networks Feature extraction Generators

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Low Dose CT image Denoising Using Boosting Attention Fusion GAN with Perceptual Loss 43

Low Dose CT Image Denoising Using Boosting Attention Fusion ...

引用

43rd Annual International conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (IEEE EMBC)

作者： Marcos, Luella Alirezaie, Javad Babyn, Paul Ryerson Univ Dept Elect & Comp Engn Toronto ON M5B 2K3 Canada Royal Univ Hosp Univ Saskatoon Hlth Reg Dept Med Imaging Saskatoon SK Canada

ISBN: (纸本)9781728111797

image denoising of Low-dose computed tomography (LDCT) images has continues to receive attention in the research community due to ongoing concerns about high-dose radiation exposure of patients for diagnosis. The use of low radiation CT image, however, could lead to inaccurate diagnosis due to the presence of noise. Deep learning techniques are being integrated into denoising methods to address this problem. In this paper, a General Adversarial Network (GAN) composed of boosting fusion of spatial and channel attention modules is proposed. These modules are embedded in the denoiser to address the limitations of other GAN-based denoising models that tend to only focus on the local processing and neglect the dependencies of creating feature maps with spatial- and channel- wise image characteristics. This study aims to preserve structural details of LDCT images by applying boosting attention modules, prevents edge over-smoothing by integrating perceptual loss via VGG16 pre-trained network, and finally, improves the computational efficiency by taking advantage of deep learning techniques and GPU parallel computation.

关键词： Medical imaging Computed tomography image denoising Generative adversarial network Attention fusion

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An SGX and Blockchain-Based System for Federated Learning in Classifying COVID-19 images

An SGX and Blockchain-Based System for Federated Learning in...

引用

IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Sihang Qin Xianjun Gu Xing Jun Haitao Li Mengqi Huang Weiqi Dai Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China State Grid Wuhan Power Supply Company China

Federated learning, an emerging distributed learning framework, has gained prominence recently. However, two challenges arise when applying federated learning to medical data. The first challenge stems from the inability of model-generating nodes to ensure computational fairness. The second challenge involves the potential leakage of private information through gradient differences. This paper introduces a federated learning system that leverages Software Guard Extensions (SGX) and blockchain technology to enhance the fairness and security of federated learning for analyzing chest imaging data. The modeling of imaging factors for chest radiography data can effectively regulate the data quality of semi-trusted nodes. A blockchain smart contract is employed for data quality-aware proof, and the quality-aware block undergoes a two-stage rewriting process across distinct nodes. A notary membership model achieves notarized cluster consensus for imaging informatics data. The multi-channel layer model quantifies the distribution of computational workload across participating nodes within the medical consortium. Upon successful verification of the enclave command in the aggregation cycle, traceable and packaged signatures are generated to support computational fairness. To ensure robust information security, SGX remote authentication is used between downstream and upstream nodes. The federated learning system, built upon SGX and blockchain, has been implemented using the Fabric federated chain platform. The ResNet model underwent training through cross-sectional federated learning to effectively classify novel coronavirus lung medical image data. Following this, multiple experimental control and security analyses were conducted, revealing that the time loss incurred due to crossing channels is approximately 21.31%, while the time overhead of SGX remains within 5.2%, as allowed by the number of nodes.

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Feature and Performance Comparison of FaaS Platforms

Feature and Performance Comparison of FaaS Platforms

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IEEE International conference on Software Engineering and Service Sciences (ICSESS)

作者： Penghui Ma Peichang Shi Guodong Yi National Key Laboratory of Parallel and Distributed Processing College of Computer Science National University of Defense Technology Changsha China Key Laboratory of Software Engineering for Complex Systems College of Computer Science National University of Defense Technology Changsha China Xiangjiang Lab Changsha China School of Advanced Interdisciplinary Studies Hunan University Of Technology and Business Changsha China

With serverless computing offering more efficient and cost-effective application deployment, the diversity of serverless platforms presents challenges to users, including platform lock-in and costly migration. Moreover, due to the black box nature of function computing, traditional performance benchmarking methods are not applicable, necessitating new studies. This article presents a detailed comparison of six major public cloud function computing platforms and introduces a benchmarking framework for function computing performance. This framework aims to help users make comprehensive comparisons and select the most suitable platform for their specific needs.

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2022 IEEE 13th Annual Ubiquitous Computing, Electronics and Mobile Communication conference, UEMCON 2022

2022 IEEE 13th Annual Ubiquitous Computing, Electronics and ...

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13th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication conference, UEMCON 2022

ISBN: (纸本)9781665492997

The proceedings contain 107 papers. The topics discussed include: supervised learning algorithm based on spike back reconstruction;cross-correlation for streaming seismic time series to detect events using parallel and real-time methods;processing of a continuous data stream on a mobile device;object detection based management system of solid waste using artificial intelligence techniques;deep reinforcement learning with IoT system characterization and knowledge adaptation;liver cirrhosis prediction using machine learning approaches;marketing in the metaverse and the need for consumer protections;veteran status as a potent determinant of misinformation and disinformation cyber risk;image enhancement for improved OCR and deck segmentation in shipbuilding document images;meta reinforcement learning with Hebbian learning;intelligent system for skin disease detection of dogs with ontology based clinical information extraction;wearable at-home vital sign monitoring;implementation of an augmented reality application to improve learning in elementary school children through playful games;using data science tools for investigating chat logs from the Conti ransomware group;personalized news recommendation with CNN and multi-head self-attention;a robust objective focused algorithm to detect source code plagiarism;and what retention rate is expected for your institution?.

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