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Performance Improvement in Rayleigh Faded Channel using Deep Learning 7

Performance Improvement in Rayleigh Faded Channel using Deep...

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7th International conference on Computing, Communications and Informatics (ICACCI)

作者： Ganesh, Sriram Sunder, Sayee, V Thakre, Arpita Amrita Vishwa Vidyapeetham Amrita Sch Engn Dept Elect & Commun Engn Bengaluru India

ISBN: (纸本)9781538653142

A neural network representing an end to end communication system is an exciting concept. This study has been made only for systems affected by Additive White Gaussian Noise where the neural network's performance is comparable to that of a communication system that uses conventional signal processing techniques at the transmitter and at the receiver. We in this paper represent a very fast time varying Rayleigh faded wireless system by a feed forward deep neural network. A comparison of the Block Error Rate vs Bit-Energy to Noise Ratio plot of the neural network with that of a conventional wireless system shows significant improvement in Block Error Rate. We use the t-distributed Stochastic Neighbourhood Embedding algorithm which reveals that Deep Learning suggests using a non-regular and non-symmetric two dimensional constellation for a Rayleigh faded channel. The effect of hyper parameters of the neural network on the performance of the end-to-end communication system has been studied and analysed in details here as well.

关键词： Deep Learning Feed Forward neural network t-SNE Rayleigh fading Signal Constellation Wireless Communication

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Comparative Study of distributed Deep Learning Tools on Supercomputers 18th

Comparative Study of Distributed Deep Learning Tools on Supe...

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18th International conference on Algorithms and Architectures for Parallel processing (ICA3PP)

作者： Du, Xin Kuang, Di Ye, Yan Li, Xinxin Chen, Mengqiang Du, Yunfei Wu, Weigang Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou Peoples R China Guangdong Prov Key Lab Big Data Anal & Proc Guangzhou Peoples R China Minist Educ Key Lab Machine Intelligence & Adv Comp Guangzhou Peoples R China

ISBN: (纸本)9783030050511;9783030050504

With the growth of the scale of data set and neural networks, the training time is increasing rapidly. distributed parallel training has been proposed to accelerate deep neural network training, and most efforts are made on top of GPU clusters. This paper focuses on the performance of distributed parallel training in CPU clusters of supercomputer systems. Using resources at the supercomputer system of "Tianhe-2", we conduct extensive evaluation of the performance of popular deep learning tools, including Caffe, TensorFlow, and BigDL, and several deep neural network models are tested, including AutoEncoder, LeNet, AlexNet and ResNet. The experiment results show that Caffe performs the best in communication efficiency and scalability. BigDL is the fastest in computing speed benefiting from its optimization for CPU, but it suffers from long communication delay due to the dependency on MapReduce framework. The insights and conclusions from our evaluation provides significant reference for improving resource utility of supercomputer resources in distributed deep learning.

关键词： distributed deep learning Tianhe-2 Speedup Performance evaluation Parallel processing

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Model-Aware Parallelization Strategy for Deep neural networks' distributed Training

Model-Aware Parallelization Strategy for Deep Neural Network...

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International conference on Advanced Cloud and Big Data (CBD)

作者： Zhaoyi Yang Fang Dong Southeast University Nanjing China

It is standard practice today to parallelize deep neural networks' (DNNs') training across distributed clusters. The most common parallelization strategy is data parallelism, but its speedup is limited by tremendous communication overhead. To alleviate this problem, we employ hybrid parallelism, where the size of data to be transferred is decreased. Hybrid parallelism divides machines into groups. Machines in the same group maintain different parts of the model and carry out training tasks collaboratively, and all the groups perform computations simultaneously. Based on hybrid parallelism, we propose a Model-aware Parallelization Strategy, with the purpose of completing the training task as quickly as possible. To determine which strategy to use, we formulate a combinatorial optimization problem and use a greedy heuristic algorithm to decide how to group machines and how to partition the network. We evaluate our Model-aware Parallelization Strategy with six frequently-used deep neural networks. Experimental results show that, for large networks such as VGG, the Model-aware Parallelization Strategy reduces completion time by more than 20 percent.

关键词： Parallel processing Computational modeling Training Data models neural networks Graphics processing units Shape

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distributed deep learning for robust multi-site segmentation of CT imaging after traumatic brain injury

Distributed deep learning for robust multi-site segmentation...

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conference on Medical Imaging: Image processing

作者： Remedios, Samuel Roy, Snehashis Blaber, Justin Bermudez, Camilo Nath, Vishwesh Patel, Mayur B. Butman, John A. Landman, Bennett A. Pham, Dzung L. Henry Jackson Fdn Ctr Neurosci & Regenerat Med Bethesda MD 20817 USA NIH Radiol & Imaging Sci Clin Ctr Bldg 10 Bethesda MD 20892 USA Middle Tennessee State Univ Dept Comp Sci Murfreesboro TN 37130 USA Vanderbilt Univ Dept Elect Engn Nashville TN 37235 USA Vanderbilt Univ Dept Biomed Engn Nashville TN 37235 USA Vanderbilt Univ Dept Comp Sci Nashville TN 37235 USA Vanderbilt Univ Med Ctr Dept Surg Nashville TN USA Vanderbilt Univ Med Ctr Dept Neurosurg Nashville TN USA Vanderbilt Univ Med Ctr Dept Hearing & Speech Sci Nashville TN USA Vanderbilt Univ Med Ctr Vanderbilt Brain Inst Ctr Hlth Serv Res Nashville TN USA Vanderbilt Univ Med Ctr Crit Illness Brain Dysfunct & Survivorship Ctr Nashville TN USA Dept Vet Affairs Med Ctr VA Tennessee Valley Healthcare Syst Nashville TN 37212 USA

ISBN: (纸本)9781510625464

Machine learning models are becoming commonplace in the domain of medical imaging, and with these methods comes an ever-increasing need for more data. However, to preserve patient anonymity it is frequently impractical or prohibited to transfer protected health information (PHI) between institutions. Additionally, due to the nature of some studies, there may not be a large public dataset available on which to train models. To address this conundrum, we analyze the efficacy of transferring the model itself in lieu of data between different sites. By doing so we accomplish two goals: 1) the model gains access to training on a larger dataset that it could not normally obtain and 2) the model better generalizes, having trained on data from separate locations. In this paper, we implement multi-site learning with disparate datasets from the National Institutes of Health (NIH) and Vanderbilt University Medical Center (VUMC) without compromising PHI. Three neural networks are trained to convergence on a computed tomography (CT) brain hematoma segmentation task: one only with NIH data, one only with VUMC data, and one multi-site model alternating between NIH and VUMC data. Resultant lesion masks with the multi-site model attain an average Dice similarity coefficient of 0.64 and the automatically segmented hematoma volumes correlate to those done manually with a Pearson correlation coefficient of 0.87, corresponding to an 8% and 5% improvement, respectively, over the single-site model counterparts.

关键词： multi-site distributed deep learning neural network computed tomography (CT) hematoma lesion segmentation

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Detection of Common English Grammar Usage Errors 15th

Detection of Common English Grammar Usage Errors

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15th International Beyond Databases, Architectures and Structures Scientific conference (BDAS)

作者： Immes, Luke Levkowitz, Haim Univ Massachusetts Lowell Comp Sci Dept Lowell MA 01854 USA

ISBN: (纸本)9783030190934;9783030190927

Our research aims to provide writers with automated tools to detect grammatical usage errors and thus improve their writing. Correct English usage is often lacking in scientific and industry papers. [16] has compiled 130 common English usage errors. We address the automated detection of these errors, and their variations, that writers often make. Grammar checkers, e.g., [9] and [11], also implement error detection. Other researchers have employed machine learning and neural networks to detect errors. We parse only the part of speech (POS) tags using different levels of generality of POS syntax and word-sense semantics. Our results provide accurate error detection and are feasible for a wide range of errors. Our algorithm specifies precisely the ability to increase or decrease the generality in order to prevent a large number of false positives. We derive this observation as a result of using The Brown corpus, which consists of 55, 889 untagged sentences, covering most genres of English usage, both fiction and non-fiction. This corpus was much larger than any corpus employed by related researchers. We implemented 80 of Swan's most common 130 rules;and detected 35 true positives distributed among 15 of Swan's rules. Such a low true positive rate, 35/55889, had been expected. No false positives were detected. We employed a separate, smaller, test suite of both true positive and true negative examples. Our system, as expected, correctly detected errors in all the true positive examples, and ignored all the true negative ones. The Language-Tool system had a detection rate of 28/130= 22%;Gram-marly had a detection rate of 60/130= 46%. Our results show significant improvement in the detection of common English usage errors.

关键词： Big data English grammar Part of speech tag Heuristic Rule Converge Machine learning neural network English usage POS NER Common error Uncommon error Context Grammatical usage rule False positive True positive Brown corpus

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Pipe-SGD: A Decentralized Pipelined SGD Framework for distributed Deep Net Training 32

Pipe-SGD: A Decentralized Pipelined SGD Framework for Distri...

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32nd conference on neural Information processing Systems (NIPS)

作者： Li, Youjie Yu, Mingchao Li, Songze Avestimehr, Salman Kim, Nam Sung Schwing, Alexander Univ Illinois Urbana IL 61801 USA Univ Southern Calif Los Angeles CA USA

distributed training of deep nets is an important technique to address some of the present day computing challenges like memory consumption and computational demands. Classical distributed approaches, synchronous or asynchronous, are based on the parameter server architecture, i.e., worker nodes compute gradients which are communicated to the parameter server while updated parameters are returned. Recently, distributed training with AllReduce operations gained popularity as well. While many of those operations seem appealing, little is reported about wall-clock training time improvements. In this paper, we carefully analyze the AllReduce based setup, propose timing models which include network latency, bandwidth, cluster size and compute time, and demonstrate that a pipelined training with a width of two combines the best of both synchronous and asynchronous training Specifically, for a setup consisting of a four-node GPU cluster we show wall-clock time training improvements of up to 5.4 x compared to conventional approaches.

关键词： Clocks

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DDoS Attack Detection Algorithm Based on Hybrid Traffic Prediction Model 8

DDoS Attack Detection Algorithm Based on Hybrid Traffic Pred...

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IEEE International conference on Signal processing, Communications and Computing (ICSPCC)

作者： Su, Yuze Meng, Xiangru Meng, Qingwei Han, Xiaoyang Air Force Engn Univ Informat & Nav Coll Xian Shaanxi Peoples R China

ISBN: (纸本)9781538679463

In order to detect distributed Denial of Service (DDoS) attack accurately and rapidly, a DDoS attack detection algorithm based on hybrid traffic prediction model (DADA-HTPM) is proposed. Firstly, the hybrid traffic prediction model (HTPM) is proposed, in which the local projection, phase space reconstruction and RBF neural network technology arc all used to restore the chaos of network traffic and train the network traffic samples to predict the future network traffic accurately. Then, the DDoS attack detection algorithm is developed based on two thresholds to reduce the false alerts caused by environment noise. Simulation results show that the DDoS attack detection algorithm based on hybrid traffic prediction model presented in this paper not only has higher traffic prediction accuracy and lower complexity, but also can detect DDoS attack quickly and accurately.

关键词： DDoS attack detection traffic prediction local projection phase space reconstruction optimal sample selection RBF neural network

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Dither NN: An Accurate neural network with Dithering for Low Bit-Precision Hardware 17

Dither NN: An Accurate Neural Network with Dithering for Low...

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17th International conference on Field-Programmable Technology (FPT)

作者： Ando, Kota Ueyoshi, Kodai Oba, Yuka Hirose, Kazutoshi Uematsu, Ryota Kudo, Takumi Ikebe, Masayuki Asai, Tetsuya Takamaeda-Yamazaki, Shinya Motomura, Masato Hokkaido Univ Grad Sch Informat Sci & Technol Sapporo Hokkaido 0600814 Japan

ISBN: (纸本)9781728102139

Energy-constrained neural network processing is in high demanded for various mobile applications. Binary neural network aggressively enhances the computational efficiency, and in contrast, it suffers from degradation of accuracy due to its extreme approximation. We propose a novel accurate neural network model based on binarization and "dithering" that distributes the quantization error to neighboring pixels. The quantization errors in the binarization are distributed in the plane, so that a pixel in the multi-level source expression more accurately represented in the resulting binarized plane by multiple pixels. We designed a low-overhead binary-based hardware architecture for the proposed model. The evaluation results show that this method can be realized with a few additional lightweight hardware components.

关键词： approximate neural network binary neural network dithering error diffusion FPGA hardware oriented algorithm neural network quantized neural network

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Multi-stage Gradient Compression: Overcoming the Communication Bottleneck in distributed Deep Learning 25th

Multi-stage Gradient Compression: Overcoming the Communicati...

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25th International conference on neural Information processing (ICONIP)

作者： Lu, Qu Liu, Wantao Han, Jizhong Guo, Jinrong Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China

ISBN: (纸本)9783030041670;9783030041663

Due to the huge size of deep learning model and the limited bandwidth of network, communication cost has become a salient bottleneck in distributed training. Gradient compression is an effective way to relieve the pressure of bandwidth and increase the scalability of distributed training. In this paper, we propose a novel gradient compression technique, Multi-Stage Gradient Compression (MGC) with Sparsity Automatic Adjustment and Gradient Recession. These techniques divide the whole training process into three stages which fit different compression strategy. To handle error and preserve accuracy, we accumulate the quantization error and sparse gradients locally with momentum correction. Our experiments show that MGC achieves excellent compression ratio up to 3800x without incurring accuracy loss. We compress gradient size of ResNet-50 from 97MB to 0.03MB, for AlexNet from 233MB to 0.06 MB. We even get a better accuracy than baseline on GoogLeNet. Experiments also show the significant scalability of MGC.

关键词： Gradient compression Communication optimization distributed system network bottleneck Deep learning

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Sign language production using neural machine translation and generative adversarial networks 29

Sign language production using neural machine translation an...

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29th British Machine Vision conference, BMVC 2018

作者： Stoll, Stephanie Camgoz, Necati Cihan Hadfield, Simon Bowden, Richard Centre for Vision Speech and Signal Processing University of Surrey Guildford United Kingdom

We present a novel approach to automatic Sign Language Production using state-of-the-art neural Machine Translation (NMT) and Image Generation techniques. Our system is capable of producing sign videos from spoken language sentences. Contrary to current approaches that are dependent on heavily annotated data, our approach requires minimal gloss and skeletal level annotations for training. We achieve this by breaking down the task into dedicated sub-processes. We first translate spoken language sentences into sign gloss sequences using an encoder-decoder network. We then find a data driven mapping between glosses and skeletal sequences. We use the resulting pose information to condition a generative model that produces sign language video sequences. We evaluate our approach on the recently released PHOENIX14T Sign Language Translation dataset. We set a baseline for text-to-gloss translation, reporting a BLEU-4 score of 16.34/15.26 on dev/test sets. We further demonstrate the video generation capabilities of our approach by sharing qualitative results of generated sign sequences given their skeletal correspondence. © 2018. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.

关键词： Generative adversarial networks

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