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Low-Rate Universal Vector Quantization for Federated Learning

Low-Rate Universal Vector Quantization for Federated Learnin...

引用

IEEE International conference on Communications Workshops, ICC

作者： Guan-Huei Lyu Bagus Aris Saputra Stefano Rini Chung-Hsuan Sun Shih-Chun Lin National Taiwan University (NTU) Taiwan National Taiwan University of Science and Technology (NTUST) Taiwan National Yang-Ming Chiao-Tung University (NYCU) Taiwan

ISBN: (数字)9798350304053

ISBN: (纸本)9798350304060

Federated learning (FL) is a distributed training paradigm in which the training of a machine learning model is coordinated by a central parameter server (PS) while the data is distributed across multiple edge devices. Thus, FL has received considerable attention as it allows for the training of models while providing security and privacy. In practice, the performance bottleneck is the link capacity from each edge device to the PS. To satisfy stringent link capacity constraints, model updates need to be compressed rather aggressively at the edge devices. In this paper, we propose a low-rate universal vector quantizer that can attain low or even fractional-rate compression. Our scheme consists of two steps: (i) model update pre-processing and (ii) vector quantization using a universal trellis coded quantizer (TCQ). In the pre-processing steps, model updates are sparsified and scaled, so as to match the TCQ design. Then, the quantization step uses TCQ, which allows for a fractional compression rate and has a flexible input size so that it can be adapted to the different neural network layers. The simulations show that our vector quantization can save 75% link capacity and still have competing accuracy, as compared with the other compressors proposed in the literature.

关键词： Training Adaptation models Accuracy Federated learning Vector quantization conferences neural networks

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Accelerating Sample-based GNN Training by Feature Caching on GPUs 7

Accelerating Sample-based GNN Training by Feature Caching on...

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7th IEEE International conference on Smart Cloud, SmartCloud 2022

作者： He, Yuqi Lai, Zhiquan Ran, Zhejiang Zhang, Lizhi Li, Dongsheng National University of Defense Technology National Key Laboratory of Parallel and Distributed Processing College of Computer Changsha China

ISBN: (数字)9781665451796

ISBN: (纸本)9781665451796

The existing graph neural network (GNN) systems adopt sample-based training on large-scale graphs over multiple GPUs. Although they support large-scale graph training, large data loading overhead is still a bottleneck. In this work, we propose SCGraph, a method that supports GPU high-speed feature caching. We classify the graph vertices sorted by out-degrees. For high out-degree vertices, we set grading caches via different GPUs to increase the overall cache content through NVLink high-speed data transmission between them. For low out-degree vertices, we expand training vertices' neighborhood in advance to regenerate cache. We evaluate SCGraph against two state-of-the-art industrial GNN frameworks, i.e., DGL and PaGraph on two datasets Reddit and ogbn-products. Experimental results show that SCGraph achieves up to 1.83× performance speedup over the state-of-the-art baselines. © 2022 IEEE.

关键词： Graph neural networks

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NeurST: neural Speech Translation Toolkit 59

NeurST: Neural Speech Translation Toolkit

引用

Joint conference of 59th Annual Meeting of the Association-for-Computational-Linguistics (ACL) / 11th International Joint conference on Natural Language processing (IJCNLP) / 6th Workshop on Representation Learning for NLP (RepL4NLP)

作者： Zhao, Chengqi Wang, Mingxuan Dong, Qianqian Ye, Rong Li, Lei ByteDance AI Lab Shanghai Peoples R China

ISBN: (纸本)9781954085565

NeurST is an open-source toolkit for neural speech translation. The toolkit mainly focuses on end-to-end speech translation, which is easy to use, modify, and extend to advanced speech translation research and products. NeurST aims at facilitating the speech translation research for NLP researchers and building reliable benchmarks for this field. It provides step-by-step recipes for feature extraction, data preprocessing, distributed training, and evaluation. In this paper, we will introduce the framework design of NeurST and show experimental results for different benchmark datasets, which can be regarded as reliable baselines for future research. The toolkit is publicly available at https://github. com/bytedance/neurst and we will continuously update the performance of NeurST with other counterparts and studies at https: //***/.

关键词： HTTP

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PB-UAP: Hybride Universal Adversarial Attack for Image Segmentation

PB-UAP: Hybride Universal Adversarial Attack for Image Segme...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Yufei Song Ziqi Zhou Minghui Li Xianlong Wang Hangtao Zhang Menghao Deng Wei Wan Shengshan Hu Leo Yu Zhang 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 School of Computer Science and Technology Huazhong University of Science and Technology School of Software Engineering Huazhong University of Science and Technology School of Information and Communication Technology Griffith University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

With the rapid advancement of deep learning, the model robustness has become a significant research hotspot, i.e., adversarial attacks on deep neural networks. Existing works primarily focus on image classification tasks, aiming to alter the model’s predicted labels. Due to the output complexity and deeper network architectures, research on adversarial examples for segmentation models is still limited, particularly for universal adversarial perturbations. In this paper, we propose a novel universal adversarial attack method designed for segmentation models, which includes dual feature separation and low-frequency scattering modules. The two modules guide the training of adversarial examples in the pixel and frequency space, respectively. Experiments demonstrate that our method achieves high attack success rates surpassing the state-of-the-art methods, and exhibits strong transferability across different models.

关键词： Training Image segmentation Perturbation methods Semantics Scattering Signal processing Predictive models network architecture Robustness Speech processing

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A Data-Loader Tunable Knob to Shorten GPU Idleness for distributed Deep Learning 15

A Data-Loader Tunable Knob to Shorten GPU Idleness for Distr...

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15th IEEE International conference on Cloud Computing (IEEE CLOUD) / IEEE World Congress on Services (IEEE SERVICES)

作者： Jia, Danlin Yuan, Geng Lin, Xue Mi, Ningfang Northeastern Univ Boston MA 02115 USA

ISBN: (数字)9781665481373

ISBN: (纸本)9781665481373

Deep neural network (DNN) has been applied as an effective machine learning algorithm to tackle problems in different domains. However, training a sophisticated DNN model takes days to weeks and becomes a challenge in constructing research on large-scale DNN models. distributed Deep Learning (DDL) contributes to accelerating DNN training by distributing training workloads across multiple computation accelerators (e.g., GPUs). Although a surge of research works has been devoted to optimizing DDL training, the impact of data-loading on GPU usage and training performance has been relatively under-explored. It is non-trivial to optimize data-loading in DDL applications that need intensive CPU and I/O resources to process enormous training data. When multiple DDL applications are deployed on a system (e.g., Cloud and HPC), the lack of a practical and efficient technique for data-loader allocation incurs GPU idleness and degrades the training throughput. Therefore, our work first focuses on investigating the impact of data-loading on the global training throughput. We then propose a throughput prediction model to predict the maximum throughput for an individual DDL training application. By leveraging the predicted results, A-Dloader is designed to dynamically allocate CPU and I/O resources to concurrently running DDL applications and use the data-loader allocation as a knob to reduce GPU idle intervals and thus improve the overall training throughput. We implement and evaluate A-Dloader in a DDL framework for a series of DDL applications arriving and completing across the runtime. Our experimental results show that A-Dloader can achieve a 23.5% throughput improvement and a 10% makespan improvement, compared to allocating resources evenly across applications.

关键词： Training Deep learning Cloud computing Runtime Computational modeling Graphics processing units Training data

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GNN-Aided distributed GAN with Partially Observable Social Graph

GNN-Aided Distributed GAN with Partially Observable Social G...

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IEEE conference on Wireless Communications and networking

作者： Ming Chen Shangshang Wang Tianyi Zhang Ziyu Shao Yang Yang School of Information Science and Technology ShanghaiTech University Shanghai China Hong Kong University of Science and Technology Guangzhou China

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

The proliferation of edge computing has facilitated the edge-based artificial intelligence-generated content (AIGC) for ubiquitous and distributed end devices. To exemplify, we focus on the distributed implementation of one established instance, generative adversarial network (GAN), yielding the distributed GAN task. Practically speaking, this task usually is impeded by concerns including the unknown latency (of processing and transmission), the fairness requirement induced by heterogeneous distributed data and the limited energy budget of end devices. Besides, an often neglected factor is how to exploit feedback from networked end devices among which social ties indicate the flow of shared information. In practice, such social ties are partially observable to lack of exact knowledge of users, e.g., resulted from scarce historical data and privacy issues. Under this setting, we propose an online algorithm via integration of 1) online learning aided by graph neural network (GNN), aiming to recover social ties with GNN-based edge prediction, for accelerated learning of uncertainty and 2) online control to adaptively guarantee the constraints. We theoretically show that it not only achieves a sub-linear regret with guaranteed energy consumption and fairness but also leads to a superior global GAN. We also conduct simulations to justify its outperformance over online baselines.

关键词： Training Energy consumption Uncertainty Image edge detection distributed databases Generative adversarial networks Prediction algorithms

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Convolutional neural network for 3D point clouds matching 44

Convolutional neural network for 3D point clouds matching

引用

conference Applications of Digital Image processing XLIV

作者： Voronin, Sergei Makovetskii, Artyom Voronin, Aleksei Zhernov, Dmitrii Chelyabinsk State Univ Dept Math 129 Br Kashirinykh Chelyabinsk 454001 Russia

ISBN: (数字)9781510645233

ISBN: (纸本)9781510645233;9781510645226

Geometric registration is a key task in many computational fields, including medical imaging, robotics, autonomous driving. The registration involves the prediction of a rigid motion to align one point cloud to another, potentially distorted by noise and partiality. The most popular point cloud registration algorithm, Iterative Closest Point (ICP), alternates between estimating the rigid motion based on a fixed correspondence estimate and updating the correspondences to their closest matches. Recently, the success of deep neural networks for image processing has motivated an approach to learning features on point clouds. Adaptation of deep learning to analyze point cloud data is far from straightforward. Most critically, standard deep neural network models require input data with regular structure, while point clouds are fundamentally irregular: Point positions are continuously distributed in the space, and any permutation of their ordering does not change the spatial distribution. Several neural networks have recently been proposed for analyzing point clouds data such as PointNet and DGCNN. In this paper, we propose a permutation invariant neural network to identify matching pairs of points in the clouds. Computer simulation results are provided to illustrate the performance of the proposed algorithm.

关键词： iterative closest points (ICP) shape analysis neural networks point-based models point cloud classification segmentation

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Prototyping of Low-Cost Configurable Sparse neural processing Unit with Buffer and Mixed-Precision Reshapeable MAC Array

Prototyping of Low-Cost Configurable Sparse Neural Processin...

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International conference on Parallel and distributed Systems (ICPADS)

作者： Binyi Wu Wolfgang Furtner Bernd Waschneck Christian Mayr Faculty of Electrical and Computer Engineering Technische Universit¨at Dresden Dresden Germany Division of Power and Sensor Systems Infineon Technologies Munich Germany Centre for Tactile Internet with Human-in-the-loop (CeTI) Technische Universität Dresden Dresden Germany

More recently, it has become possible to run deep learning algorithms on edge devices such as microcontrollers due to continuous improvements in neural network optimization algorithms such as quantization and neural architecture search. Nonetheless, most of the embedded hardware available today still falls short of the requirements of running deep neural networks. As a result, specialized processors have emerged to improve the inference efficiency of deep learning algorithms. However, most are not for edge applications that require efficient and low-cost hardware. Therefore, we design and prototype a low-cost configurable sparse neural processing Unit (NPU). The NPU has a built-in buffer and a reshapable mixed-precision multiply-accumulator (MAC) array. The computing and memory resources of the NPU are parameterized, and different NPUs can be derived. Besides, users can also conFigure the NPU at runtime to fully utilize the resources. In our experiments, the 200MHz NPU with only 32 MACs is more than 32 times faster than the 400MHzSTM32H7 when inferring MobileNet-Vl. Besides, the yielded NPUs can achieve roofline or even beyond roofline performance. The buffer and reshapeable MAC array push the NPU’s attainable performance to the roofline, while the feature of supporting sparsity allows the NPU to obtain performance beyond the roofline.

关键词： Deep learning Runtime Quantization (signal) Program processors neural networks Random access memory Prototypes

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TABot – A distributed Deep Learning Framework for Classifying Price Chart Images 10th

TABot – A Distributed Deep Learning Framework for Classifyi...

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10th International Advanced Computing conference, IACC 2020

作者： Siper, Matthew Makinen, Kyle Kanan, Raman CS6513 CSE NYU Tandon School of Engineering New York University BrooklynNY11201 United States

ISBN: (纸本)9789811604003

In this work, we propose a framework called TABot, a distributed deep learning (DL) framework for classifying price chart images. The deep learning engine we outline consists of an ensemble of convolutional neural networks employed for classifying price chart images. We present three workflows that compose our framework. The first is the data sourcing workflow: a distributed asynchronous pipeline to collect price data and programmatically generate candlestick charts. We measure the processing times of our distributed solution relative to a synchronous analog to identify a nominal processing time differential between the two solutions. The second is the model training workflow. We again leverage a distributed asynchronous pipeline to train each convolutional neural network in a parallel fashion. We measure the processing time of our parallel solution and compare to a synchronous analog. The third is the prediction workflow. We introduce a simple scheme for collecting the prediction output of each component model in an ensemble network model. Our results support the viability of convolutional neural networks to classify price chart images. The TAbot architecture additionally highlights the benefit of utilizing elastic computing environments to manage computational and data persistence costs incurred by deep learning frameworks. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Pipelines

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Design of Real-time Object Recognition Algorithm Based on Deep Convolution neural network

Design of Real-time Object Recognition Algorithm Based on De...

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Internet of Things, Robotics and distributed Computing (ICIRDC), International conference on

作者： Shuqin Wang Zibo Technician College Zibo China

In recent years, with the rapid development of computer vision and deep learning technology, real-time object recognition has become more and more important in various application fields. This research is devoted to designing a real-time object recognition algorithm based on Deep Convolution neural network (DCNN), aiming at overcoming the trade-off between accuracy and real-time performance of traditional methods. In this study, a DCNN is trained by using a large-scale labeled data set, so that it can learn rich feature representation. By introducing the hierarchical structure of deep network, the abstract ability of the model to objects is improved, and its generalization performance is enhanced by data enhancement technology. Through careful design of network structure and parameter adjustment, the model performs well in real-time object recognition tasks. In order to optimize real-time processing, we adopt a series of acceleration and optimization strategies to ensure that the algorithm can run quickly and reliably in various environments. Experiments show that the proposed algorithm achieves superior performance on several public data sets. Compared with traditional methods, the accuracy is improved by more than 8.26%, while maintaining high efficiency in real-time. The real-time object recognition algorithm based on DCNN proposed in this study has important potential value in practical application, which provides new technical support for promoting the development and application of intelligent systems.

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