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Scalable Deep Learning on distributed Infrastructures: Challenges, Techniques, and Tools

ACM COMPUTING SURVEYS 2020年第1期53卷 3-3页

作者： Mayer, Ruben Jacobsen, Hans-Arno Tech Univ Munich Boltzmannstr 3 D-85748 Garching Germany

Deep Learning (DL) has had an immense success in the recent past, leading to state-of-the-art results in various domains, such as image recognition and natural language processing. One of the reasons for this success is the increasing size of DL models and the proliferation of vast amounts of training data being available. To keep on improving the performance of DL, increasing the scalability of DL systems is necessary. In this survey, we perform a broad and thorough investigation on challenges, techniques and tools for scalable DL on distributed infrastructures. This incorporates infrastructures for DL, methods for parallel DL training, multi-tenant resource scheduling, and the management of training and model data. Further, we analyze and compare 11 current open-source DL frameworks and tools and investigate which of the techniques are commonly implemented in practice. Finally, we highlight future research trends in DL systems that deserve further research.

关键词： Deep-learning systems

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Transformer in UAV image-Based Weed Mapping

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REMOTE SENSING 2023年第21期15卷 5165-5165页

作者： Zhao, Jiangsan Berge, Therese With Geipel, Jakob Norwegian Inst Bioecon Res NIBIO Dept Agr Technol POB 115 NO-1431 As Norway Norwegian Inst Bioecon Res NIBIO Dept Invertebrate Pests & Weeds Forestry Agr & Hort POB 115 NO-1431 As Norway

Weeds affect crop yield and quality due to competition for resources. In order to reduce the risk of yield losses due to weeds, herbicides or non-chemical measures are applied. Weeds, especially creeping perennial species, are generally distributed in patches within arable fields. Hence, instead of applying control measures uniformly, precision weeding or site-specific weed management (SSWM) is highly recommended. Unmanned aerial vehicle (UAV) imaging is known for wide area coverage and flexible operation frequency, making it a potential solution to generate weed maps at a reasonable cost. Efficient weed mapping algorithms need to be developed together with UAV imagery to facilitate SSWM. Different machine learning (ML) approaches have been developed for image-based weed mapping, either classical ML models or the more up-to-date deep learning (DL) models taking full advantage of parallel computation on a GPU (graphics processing unit). Attention-based transformer DL models, which have seen a recent boom, are expected to overtake classical convolutional neural network (CNN) DL models. This inspired us to develop a transformer DL model for segmenting weeds, cereal crops, and 'other' in low-resolution RGB UAV imagery (about 33 mm ground sampling distance, g.s.d.) captured after the cereal crop had turned yellow. images were acquired during three years in 15 fields with three cereal species (Triticum aestivum, Hordeum vulgare, and Avena sativa) and various weed flora dominated by creeping perennials (mainly Cirsium arvense and Elymus repens). The performance of our transformer model, 1Dtransformer, was evaluated through comparison with a classical DL model, 1DCNN, and two classical ML methods, i.e., random forest (RF) and k-nearest neighbor (KNN). The transformer model showed the best performance with an overall accuracy of 98.694% on pixels set aside for validation. It also agreed best and relatively well with ground reference data on total weed coverage, R2 = 0.598. I

关键词： 1Dtransformer perennial weed mapping low-resolution UAV image

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Efficient Volume Estimation for Dynamic Environments using Deep Learning on the Edge

Efficient Volume Estimation for Dynamic Environments using D...

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IEEE International Symposium on parallel and distributed processing Workshops and Phd Forum (IPDPSW)

作者： Chandan Kumar Yamini Mathur Ali Jannesari Department of Computer Science Iowa State University Ames IA USA

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

The utility of edge devices has increased in volume estimation of uneven terrains. Existing techniques utilize several geo-tagged images of the landscape, captured in-flight by an edge device mounted over a UAV, to generate 3D models and perform volume estimation through manual boundary marking. These methods, although accurate, require significant time, human effort and are heavily dependent on GPS. We present an efficient deep learning framework that detects the object of interest and automatically determines the volume (independent of GPS) of the detected object on-the-fly. Our method employs a stereo camera for depth sensing of the object and overlays a unit mesh grid over the object's boundary to perform volume estimation. We explore the accuracy vs computational complexity trade-off on variations of our technique. Experiments indicate that our method reduces the time for volume estimation by several orders of magnitude in contrast to existing methods and is independent of GPS as well. Also, to the best of our knowledge, this is the first method that can perform volume analysis in a dynamic environment.

关键词： Deep learning Solid modeling Three-dimensional displays Error analysis Volume measurement image edge detection Heuristic algorithms

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A Fast Adversarial Sample Detection Approach for Industrial Internet-of-Things Applications

A Fast Adversarial Sample Detection Approach for Industrial ...

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International Workshop on Quality of Service

作者： Shigeng Zhang Yudong Li Shuxin Chen Xuan Li Jian Zhang School of Computer Science and Engineering Central South University College of Computer Science and Electronic Engineering Hunan University The Science and Technology on Parallel and Distributed Processing Laboratory (PDL) China

Adversarial attacks reveal the inherent vulnerability of deep neural networks, which face serious security issues for their security. Among them, the attack against the Deep Neural Network (DNN) application used in the Industrial Internet of Things (IIoT) is a key area in adversarial attacks. Adversarial examples generated by attackers by adding human-undetectable interference to legitimate examples may cause models to make wrong decision results, resulting in serious accidents. Many detection technologies have been proposed to mitigate the harm of adversarial examples to neural networks, among which the methods based on the difference of feature attribution between normal examples and adversarial examples show state-of-the-art detection performance, but they suffer from detection efficiency. In this work, we focus on improving the detection efficiency of the feature-attribution-based detection methods. We observe that there is still a significant difference in the feature attribution distribution of a normal image and an adversarial image even only some pixels in the image are processed, which can be verified by utilizing the Kolmogorov-Smirnov test. Based on this observation, we first adopt a variety of strategies to sample partial pixels in an image and then utilize the selected pixels to train a feature-attribution-based detector for detecting adversarial examples. Extensive experiments conducted on four datasets (MNIST, CIFAR-10, SVHN, CIFAR-100) against various attacks proved that the detection efficiency of the accelerated detection method is improved (for example, the average execution time was increased by 8.7 times on CIFAR-10) while the detection performance maintains state-of-the-art.

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基于云计算的船舶图像网络数据低时延传输方法

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舰船科学技术 2024年第16期46卷 178-181页

作者：李小丽王磊河南开放大学信息工程与人工智能学院河南郑州450046 河南理工大学计算机科学与技术学院河南焦作454003

为实时传输海量船舶图像,为有效监控船舶实时航行状况提供保障,研究基于云计算的船舶图像网络数据低时延传输方法。结合云计算技术,构建船舶图像网络数据传输平台,通过平台数据接收单元将大量船舶网络数据存入云端服务器内;数据处理单... 详细信息

为实时传输海量船舶图像,为有效监控船舶实时航行状况提供保障,研究基于云计算的船舶图像网络数据低时延传输方法。结合云计算技术,构建船舶图像网络数据传输平台,通过平台数据接收单元将大量船舶网络数据存入云端服务器内;数据处理单元基于云计算的分布式处理特点,结合并行运行框架Map Reduce和并行化K-means聚类算法,并行挖掘云端服务器内的船舶图像网络数据;信道均衡单元结合所构建的船舶通信网络节点能量消耗模型,均衡船舶通信网络信道,运用均衡信道实现所挖掘船舶图像网络数据的传输。结果显示,该方法的数据挖掘速率高,数据传输过程中各信道能量开销均衡,且可低至3.5 k J,传输船舶图像网络数据的时延可低至10.23 ms,实现了数据的低时延传输。

关键词：云计算船舶图像网络数据低时延传输聚类算法信道均衡

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Design optimisation of multiplier-free parallel pipelined FFT on field programmable gate array

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IET CIRCUITS DEVICES & SYSTEMS 2020年第7期14卷 995-1000页

作者： Godi, Prasanna Kumar Krishna, Battula Tirumala Kotipalli, Pushpa Jawaharlal Nehru Technol Univ Elect & Commun Engn Kakinada India Shri Vishnu Engn Coll Women Autonomous Elect & Commun Engn Bhimavaram India

Fast Fourier transform (FFT) is utilised to minimise the complexity of discrete Fourier transform by converting signals from frequency domain to time domain and conversely. Digital signal processing systems like image processing, general filtering, sonar, spread-spectrum communications and convolutions use this FFT operations. Radix-2 decimation in frequency (R2DIF) method is designed to execute an efficient FFT architecture in this study. Each and every state of the FFT stores the input and output the data using the R2DIF method. Also, the complex twiddle factors in FFT are replaced by the proposed uniform Montgomery algorithm. This technique simply performs the shift-add method instead of the multiplication process which also enhances the convergence of the calculation. So, the FFT implementation is done with the help of the proposed method which reduces the usage of chips in the process. Based on this approach, it performs the operation of FFT from 16 points to 1024 points and the performance of this proposed method is compared with existing approaches. Moreover, it does not require expensive dedicated functional blocks and uses only distributed logic resources. The simulation is carried out by the Xilinx platform using Verilog coding. The proposed design outperforms conventional methods in terms of less usage power and high speed.

关键词： discrete Fourier transforms hardware description languages pipeline processing digital signal processing chips fast Fourier transforms field programmable gate arrays mathematics computing design optimisation multiplier-free parallel field programmable gate array frequency domain time domain digital signal image processing general filtering spread-spectrum communications FFT operations radix-2 decimation frequency method efficient FFT architecture FFT stores R2DIF method complex twiddle factors uniform Montgomery algorithm shift-add method multiplication process FFT implementation

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SUPERVISED MULTI-VIEW distributed HASHING

SUPERVISED MULTI-VIEW DISTRIBUTED HASHING

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IEEE International Conference on image processing (ICIP)

作者： Tang, Yunpeng Shen, Xiaobo Ji, Zexuan Wang, Tao Fu, Peng Sun, Quan-Sen Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781728163956

Multi-view hashing efficiently integrates multi-view data for learning compact hash codes, and achieves impressive large-scale retrieval performance. In real-world applications, multi-view data are often stored or collected in different locations, where hash code learning is more challenging yet less studied. To fulfill this gap, this paper proposes a novel supervised multi-view distributed hashing (SMvDisH) for hash code learning from multi-view data in a distributed manner. SMvDisH yields the discriminative latent hash codes by joint learning of latent factor model and classifier. With local consistency assumption among neighbor nodes, the distributed learning problem is divided into a set of decentralized subproblems. The subproblems can be solved in parallel, and the computational and communication costs are low. Experimental results on three large-scale image datasets demonstrate that SMvDisH achieves competitive retrieval performance and trains faster than state-of-the-art multi-view hashing methods.

关键词： Hashing distributed learning multi-view data

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OpenMedIA: Open-Source Medical image Analysis Toolbox and Benchmark under Heterogeneous AI Computing Platforms

arXiv

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arXiv 2022年

作者： Zhuang, Jia-Xin Huang, Xiansong Yang, Yang Chen, Jiancong Yu, Yue Gao, Wei Li, Ge Chen, Jie Zhang, Tong Peng Cheng Laboratory Shenzhen China School of Computer Science and Technology Harbin Institute of Technology Shenzhen China School of Electronic and Computer Engineering Peking University China National Laboratory for Parallel and Distributed Processing National University of Defense Technology China

In this paper, we present OpenMedIA, an open-source toolbox library containing a rich set of deep learning methods for medical image analysis under heterogeneous Artificial Intelligence (AI) computing platforms. Various medical image analysis methods, including 2D/3D medical image classification, segmentation, localisation, and detection, have been included in the toolbox with PyTorch and/or MindSpore implementations under heterogeneous NVIDIA and Huawei Ascend computing systems. To our best knowledge, OpenMedIA is the first open-source algorithm library providing compared PyTorch and MindSpore implementations and results on several benchmark datasets. The source codes and models are available at https://***/ OpenMedIA. © 2022, CC BY-NC-SA.

关键词： Open systems

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Classification Of Human Facial Expressions For Emotion Recognition Using A distributed Computer System 5

Classification Of Human Facial Expressions For Emotion Recog...

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5th International Conference on Computer Science and Engineering (UBMK)

作者： Korkmaz, Timucin Erol, Hamza Mersin Univ Grad Sch Nat & Appl Sci Dept Comp Engn Mersin Turkey Mersin Univ Engn Fac Dept Comp Engn Mersin Turkey

ISBN: (纸本)9781728175652

A computer system was developed to classify human facial expressions for emotion recognition using a distributed computer architecture within the scope of big data. Computers with normal standards and features were used in the distributed computer architecture. Necessary system software for the distributed computer architecture was established and mutual communication protocols were provided between the computers and databases in the computer network. Visual C# parallel programming was used as the software language on the distributed computer architecture. In the software prepared, face image files were processed and threads were created. The threads created later were processed in the processors of the computers. The threads were run on the processors in the distributed computer system, and facial expressions were classified for emotion recognition. In the distributed computer architecture: the number of image files, the volume of big data and the load of the threads to be processed arc taken into account;and databases and parallel programming were used and the classification of human face images was performed. Emotion analysis methods and facial expression recognition techniques were used in the classification process. The distributed computer system is in low cost and high processing speed. With the distributed computer system architecture, big data analytics has become convenient and feasible.

关键词： Big data distributed System Emotion analysis image classification parallel Programing Face expression recognition

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A Counterfactual Ultrasound Anti-Interference Self-Supervised Network for B-mode Ultrasound Tongue Extraction

A Counterfactual Ultrasound Anti-Interference Self-Supervise...

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

作者： Yan Jia Yuqing Cheng Kele Xu Yong Dou Peng Qiao Zhouyu He National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology Changsha China College of Systems Engineering National University of Defense Technology Changsha China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

B-mode ultrasound tongue imaging is a non-invasive and real-time method for visualizing vocal tract deformation. However, accurately extracting the tongue’s surface contour remains a significant challenge due to the low signal-to-noise ratio (SNR) and prevalent speckle noise in ultrasound images. Traditional supervised learning models often require large labeled datasets, which are labor-intensive to produce and susceptible to noise interference. To address these limitations, we present a novel Counterfactual Ultrasound Anti-Interference Self-Supervised Network (CUAI-SSN), which integrates self-supervised learning (SSL) with counterfactual data augmentation, progressively disentangles confounding factors, ensuring that the model generalizes well across varied ultrasound conditions. Our approach leverages causal reasoning to decouple noise from relevant features, enabling the model to learn robust representations that focus on essential tongue structures. By generating counterfactual image-label pairs, our method introduces alternative, noise-independent scenarios that enhance model training. Furthermore, we introduce attention mechanisms to enhance the network’s ability to capture fine-grained details even in noisy conditions. Extensive experiments on real ultrasound tongue images demonstrate that CUAI-SSN outperforms existing methods, setting a new benchmark for automated contour extraction in ultrasound tongue imaging. Our code is publicly available at https://***/inexhaustible419/CounterfactualultrasoundAI.

关键词： Training Ultrasonic imaging Tongue Self-supervised learning Data augmentation Data models Cognition Data mining Noise measurement Signal to noise ratio

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