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Accelerating distributed Inference of Sparse Deep neural networks via Mitigating the Straggler Effect

Accelerating Distributed Inference of Sparse Deep Neural Net...

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IEEE conference on High Performance Extreme Computing (HPEC)

作者： Mohammad Hasanzadeh Mofrad Rami Melhem Yousuf Ahmad Mohammad Hammoud University of Pittsburgh Pittsburgh USA Carnegie Mellon University in Qatar Doha Qatar

ISBN: (数字)9781728192192

ISBN: (纸本)9781728192208

Once a Deep neural network (DNN) is trained, an inference algorithm retains the learning and applies it to batches of data. The trained DNN can be sparse because of pruning or following a preset sparse connectivity pattern. Inference in such sparse networks requires less space and time complexities compared to dense ones. Similar to dense DNNs, sparse DNNs can be parallelized using model or data parallelism, whereby the former partitions the network and the latter partitions the input among multiple threads. Model parallelism efficiently utilizes the Last Level Cache (LLC) but has a heavy synchronization cost because of compulsory reductions per layer. In contrast, data parallelism allows independent execution of partitions but suffers from a straggler effect due to a load imbalance among partitions. We combine data and model parallelisms through a new type of parallelism that we denote data-then-model. In data-then-model, each thread starts with data parallelism, thus mitigating the per-layer synchronization cost of model parallelism. After it finishes its partition, it switches to model parallelism to support a slower active thread, hence, alleviating the straggler effect of data parallelism. We compare data-then-model parallelism with data and model parallelisms as well as task-based parallelisms using the IEEE HPEC sparse DNN challenge dataset. On average, we achieve up to 10 to 65% speedup compared to these parallelisms.

关键词： Instruction sets neural networks distributed databases Parallel processing Data models Synchronization Load modeling

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A VIDEO CAMERA MODEL IDENTIFICATION SYSTEM USING DEEP LEARNING AND FUSION 44

A VIDEO CAMERA MODEL IDENTIFICATION SYSTEM USING DEEP LEARNI...

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44th IEEE International conference on Acoustics, Speech and Signal processing (ICASSP)

作者： Hosler, B. Mayer, O. Bayar, B. Zhao, X. Chen, C. Shackleford, J. A. Stamm, M. C. Drexel Univ Dept Elect & Comp Engn Philadelphia PA 19104 USA

ISBN: (纸本)9781479981311

While significant work has been conducted to perform source camera model identification for images, little work has been done specifically for video camera model identification. This is problematic because different forensic traces may be left in digital images and videos captured by the same camera. As our experiments in this paper will show, a system trained to perform camera model identification for images yields unacceptably low performance when given video frames from the same cameras. To overcome this problem, new systems for identifying a videos source must be developed. In this paper, we propose a deep learning based system for determining the source camera model that captured a digital video. To do this, we use a convolutional neural network to produce camera model identification scores for small patches taken from video frames. These patches are chosen by a patch selection system that obtains patches from several appropriate frames temporally distributed throughout the video. Forensic information obtained by the CNN is provided to a fusion system, which combines it to produce a single, more accurate identification result. Through a series of experiments, we evaluate several system design choices and show that our system can achieve 95.9% video camera model identification accuracy.

关键词： Deep learning convolutional neural networks multimedia forensics camera model identification

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Enhanced Word Embedding Method in Text Classification

Enhanced Word Embedding Method in Text Classification

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International conference on Big Data and Information Analytics (BigDIA)

作者： Shengze Hu Chunhui He Bin Ge Fang Liu Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology Changsha China Defense Science and Technology Strategic Research Think Tank National University of Defense Technology Changsha China

ISBN: (数字)9781665422321

ISBN: (纸本)9781665446662

For the task of natural language processing (NLP), Word embedding technology has a certain impact on the accuracy of deep neural network algorithms. Considering that the current word embedding method cannot realize the coexistence of words and phrases in the same vector space. Therefore, we propose an enhanced word embedding (EWE) method. Before completing the word embedding, this method introduces a unique sentence reorganization technology to rewrite all the sentences in the original training corpus. Then, all the original corpus and the reorganized corpus are merged together as the training corpus of the distributed word embedding model, so as to realize the coexistence problem of words and phrases in the same vector space. We carried out experiment to demonstrate the effectiveness of the EWE algorithm on three classic benchmark datasets. The results show that the EWE method can significantly improve the classification performance of the CNN model.

关键词： Training Text categorization neural networks Big Data Natural language processing Classification algorithms Task analysis

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Resource Deflation: A New Approach For Transient Resource Reclamation 19

Resource Deflation: A New Approach For Transient Resource Re...

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14th EuroSys conference

作者： Sharma, Prateek Ali-Eldin, Ahmed Shenoy, Prashant Indiana Univ Bloomington IN 47405 USA Univ Massachusetts Amherst Amherst MA USA

ISBN: (纸本)9781450362818

Data centers and clouds are increasingly offering low-cost computational resources in the form of transient virtual machines. Whenever demand for computational resources exceeds their availability, transient resources can reclaimed by preempting the transient VMs. Conventionally, these transient VMs are used by low-priority applications that can tolerate the disruption caused by preemptions. In this paper we propose an alternative approach for reclaiming resources, called resource deflation. Resource deflation allows applications to dynamically shrink (and expand) in response to resource pressure, instead of being preempted outright. Deflatable VMs allow applications to continue running even under resource pressure, and increase the utility of low-priority transient resources. Deflation uses a dynamic, multi-level cascading reclamation technique that allows applications, operating systems, and hypervisors to implement their own policies for handling resource pressure. For distributed data processing, machine learning, and deep neural network training, our multi-level approach reduces the performance degradation by up to 2x compared to existing preemption-based approaches. When deflatable VMs are deployed on a cluster, our policies allow up to 1.6x utilization without the risk of preemption.

关键词： Deep neural networks

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A sensor data processing and storage method for wireless body area networks 10

A sensor data processing and storage method for wireless bod...

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10th International conference on Information Technology in Medicine and Education, ITME 2019

作者： Han, Lu Xun, Lulu Liu, Jinfa Yin, Wenjun Zheng, Xiangwei Zheng, Zhuoran Shandong Normal University Ji'nan China Shandong Normal University Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Ji'nan China

ISBN: (纸本)9781728139173

This paper proposes an adaptive processing method and a dynamic storage method of pulsed wave signal based on wireless body area network. Firstly, the ZigBee star network model is adopted as the basic network architecture of the system. Secondly, an adaptive Gaussian filter model is designed to eliminate the influence of environmental and man-made factors. Finally, the dynamic storage algorithm is used to map the pulsed wave signal to a new stored sample, and the regression model is used to restore the new sample to the original signal. Simulation experiments show that: 1) The adaptive Gaussian model minimizes the signal noise and better maintains the characteristics of the pulsed wave signal. 2) On the premise of using ARIMA regression model of neural network, dynamic storage algorithm improves the regression accuracy compared with fixed storage algorithm. © 2019 IEEE.

关键词： Zigbee

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distributed computing framework of intelligent sensor network for electric power internet of things

Distributed computing framework of intelligent sensor networ...

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IEEE Joint International Information Technology and Artificial Intelligence conference (ITAIC)

作者： Dawei Li Ruimo Yu Chunhe Song Shu Li Gengtao Jia Xiaoli Zhou Start Grid Shanghai Information & Telecommunication Company Shanghai China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang China

ISBN: (数字)9781728152448

ISBN: (纸本)9781728152455

The power IoT terminal environment is complex, and current smart sensor sensors have problems such as poor networking capabilities and large processing delays, and few specific edge-side automatic networking capabilities. This paper analyzes and elaborates the distributed intelligent sensor computing framework from the perspective of edge intelligent networking, designs a computing model for intelligent sensors of power Internet of Things terminals, and gives the edge-based deployment of convolutional neural network architecture, data aggregation, model training, and intelligent reasoning. Finally, the validity of the proposed framework model is verified by public sample data.

关键词： Training Analytical models Computational modeling Data aggregation Data models Internet of Things Intelligent sensors

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Real Time Object Detection in Surveillance Cameras with Distance Estimation using Parallel Implementation

Real Time Object Detection in Surveillance Cameras with Dist...

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Emerging Trends in Information Technology and Engineering (ic-ETITE), conference on

作者： Meherdeep Thakur Saira Banu J. Vellore Institute of Technology Vellore India

ISBN: (数字)9781728141428

ISBN: (纸本)9781728141435

Object detection is not only shaping how Computers see and analyze things but it is also helping in the behavior of how an object reacts to the change in its environment. The main application of these object detection sensors or software is to find the location of an object in space or to track its movement. Object detection has infinitely many use cases and in this paper, we are introducing an application that will allow safety of users struck in a disaster and who need to be evacuated. In such cases the main thing to focus and to eradicate is camera noise, saturation and image compression. Our solution is to establish a connection between the person struck in a disaster with fire safety people. This works over a convolutional network that allows us to detect vulnerable things present inside a room that needs to be rescued and can also give an insight of any explosive inside the room. Our model uses Faster-RCNN and COCO which is a pretrained dataset. This allows real time object detection and classification on our network. Using this we were able to detect an object or a person and get him to rescue by providing them a shortest way out of that place. With this we were able to get an accuracy of more than 75% in our object detection model.

关键词： Object Detection RCNN Convolutional neural networks distributed Computing Parallel processing Object detection distributed computing PARALLEL processing (COMPUTERS) Detective cameras Disasters Persons

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CNNP-v2: A Memory-Centric Architecture for Low-Power CNN Processor on Domain-Specific Mobile Devices

CNNP-v2: A Memory-Centric Architecture for Low-Power CNN Pro...

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1st IEEE International conference on Artificial Intelligence Circuits and Systems (AICAS)

作者： Choi, Sungpill Bong, Kyeongryeol Han, Donghyeon You, Hoi-Jun Korea Adv Inst Sci & Technol Sch Elect Engn Daejeon 34141 South Korea

An energy-efficient memory-centric convolutional neural network (CNN) processor architecture is proposed for smart devices such as wearable devices or the internet of things (IoT) devices. To achieve energy-efficient processing, it has two key features: First, 1-D shift convolution PEs with fully distributed memory architecture achieve 1.5TOPS/W energy efficiency, and it can be boosted up equivalent 3.1TOPS/W energy efficiency with separable filter approximation and transpose-read SRAM. Compared with conventional architecture, even though it has massively parallel 1024 MAC units, it achieves high energy efficiency by scaling down the voltage to 0.46V due to its fully local routed design. Second, fully configurable 2-D mesh core-to-core interconnection support the various size of input features to maximize utilization. The proposed architecture is evaluated 16mm(2) chip, which is fabricated with a 65nm CMOS process. As a result, it performs real-time face recognition with the only 68.9mW at 40MHz and 0.6V.

关键词： Memory architecture Digital circuits Energy efficiency Integrated circuit interconnections Convolutional neural networks Face recognition Convolutional neural network (CNN) memory-centric architecture low-power image processing energy-efficient digital circuit

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Detection of DDoS based on Gray Level Co-occurrence Matrix theory and deep learning

Detection of DDoS based on Gray Level Co-occurrence Matrix t...

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International conference on Mechanical, Control and Computer Engineering (ICMCCE)

作者： Jiayu Shi Bin Wu School of Cyberspace Security Beijing University of Posts and Telecommunications National Disaster Recovery Technology Engineering Laboratory Beijing University of Posts and Telecommunications Beijing China

There have been researches on distributed Denial of Service (DDoS) attack detection based on deep learning, but most of them use the feature data processed by data mining for feature learning and classification. Based on the original data flow, this paper combines the method of Gray Level Co-occurrence Matrix (GLCM), which not only retains the original data but also can further extract the potential relationship between the original data. The original data matrix and the reconstructed matrix were taken as the input of the model, and the Convolutional neural network(CNN) was used for feature learning. Finally, the classifier model was trained for detection. The experimental part is divided into two parts: comparing the detection effect of different data processing methods and different deep learning algorithms; the effectiveness and objectivity of the proposed method are verified by comparing the detection effect of the deep learning algorithm with that of the statistical analysis feature algorithm.

关键词： Deep learning Statistical analysis distributed databases Feature extraction Denial-of-service attack Data processing Classification algorithms

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A Survey on Approaches and Applications of Knowledge Representation Learning

A Survey on Approaches and Applications of Knowledge Represe...

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IEEE International conference on Data Science in Cyberspace (DSC)

作者： Chenchen Li Aiping Li Ye Wang Hongkui Tu Yichen Song College of Computer National University of Defense Technology ChangSha China College of Computer Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology ChangSha China

ISBN: (数字)9781728195582

ISBN: (纸本)9781728195599

Knowledge representation learning (KRL) is one of the important research topics in artificial intelligence and Natural language processing. It can efficiently calculate the semantics of entities and relations in a low-dimensional space, and effectively solve the problem of data sparsity, which significantly improve the performance of knowledge acquisition, fusion and reasoning and so on. Starting from the five perspectives of distance-based, semantic matching, bilinear-based, neural network model and additional information model, this paper first introduces the overall framework and specific model design, and then correspondingly introduces the experimental evaluation tasks, metrics and benchmark datasets of each model. On this basis, how to apply KRL to various downstream tasks is summarized.

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