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Performance and Consistency Analysis for distributed Deep Learning Applications

Performance and Consistency Analysis for Distributed Deep Le...

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IEEE International conference on Performance, Computing and Communications (IPCCC)

作者： Danlin Jia Manoj Pravakar Saha Janki Bhimani Ningfang Mi Northeastern University Boston USA Florida International University Miami USA

ISBN: (数字)9781728198293

ISBN: (纸本)9781728198309

Accelerating the training of Deep neural network (DNN) models is very important for successfully using deep learning techniques in fields like computer vision and speech recognition. distributed frameworks help to speed up the training process for large DNN models and datasets. Plenty of works have been done to improve model accuracy and training efficiency, based on mathematical analysis of computations in the Con-volutional neural networks (CNN). However, to run distributed deep learning applications in the real world, users and developers need to consider the impacts of system resource distribution. In this work, we deploy a real distributed deep learning cluster with multiple virtual machines. We conduct an in-depth analysis to understand the impacts of system configurations, distribution typologies, and application parameters, on the latency and correctness of the distributed deep learning applications. We analyze the performance diversity under different model consistency and data parallelism by profiling run-time system utilization and tracking application activities. Based on our observations and analysis, we develop design guidelines for accelerating distributed deep-learning training on virtualized environments.

关键词： Deep learning Training Computational modeling neural networks Parallel processing Resource management Load modeling

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Recurrent neural network Based Intrusion Detection System

Recurrent Neural Network Based Intrusion Detection System

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International conference on Communications and Signal processing

作者： Sanchit Nayyar Sneha Arora Maninder Singh Computer Science and Engineering Department Thapar Institute of Engineering and Technology Patiala India

ISBN: (数字)9781728149882

ISBN: (纸本)9781728149899

An increase in connectivity through the internet and increased freedom of data access has led to numerous attempts to attack network servers. These attacks have become sophisticated over time and hence difficult to detect using the existing Intrusion Detection Systems. Existing research in the field of IDS has not been able to categorize DDoS attacks with a fair degree of accuracy till date. It has been extremely difficult to strike the balance between accuracy and prediction time. This paper aims at using a LSTM based Machine Learning model to detect anomalies in the network traffic and redirecting all malicious requests to a honeypot based black hole server. This algorithm was trained and tested on the CICIDS2017 data set consisting of numerous attacks such as Patator based attacks, Web based Brute Force, DoS sloworis, DoS slowhttptest, DoS Hulk, DoS GoldenEye, DDoS LOIT and some other categorical attacks. The results exhibit a high degree of accuracy in detection of these attacks, and the model is suitable for use in existing distributed servers.

关键词： Intrusion detection Computer crime Prediction algorithms Training Machine learning IP networks Recurrent neural networks

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Performance Optimizations and Analysis of distributed Deep Learning with Approximated Second-Order Optimization Method 19

Performance Optimizations and Analysis of Distributed Deep L...

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48th International conference on Parallel processing (ICPP)

作者： Tsuji, Yohei Osawa, Kazuki Ueno, Yuichiro Naruse, Akira Yokota, Rio Matsuoka, Satoshi Tokyo Inst Technol Tokyo Japan NVIDIA Tokyo Japan AIST AIST Tokyo Tech RWBC OIL Tokyo Inst Technol Global Sci Informat & Comp Ctr Tokyo Japan Tokyo Inst Technol RIKEN Ctr Computat Sci Kobe Hyogo Japan

ISBN: (纸本)9781450371964

Faster training of deep neural networks is desired to speed up the research and development cycle in deep learning. distributed deep learning and second-order optimization methods are two different techniques to accelerate the training of deep neural networks. In the previous work, researchers show that an approximated second-order optimization method, called K-FAC, can mitigate each other drawbacks of the two techniques. However, there was no detailed discussion on the performance, which is critical for the usage in practice. In this work, we propose several performance optimization techniques to reduce the overheads of K-FAC and to accelerate the overall training. Applying all performance optimizations, we are able to speed up the training 1.64 times per iteration compared to a baseline. Additional to the performance optimizations, we construct a simple performance model to predict model training performance to help the users to determine whether distributed K-FAC is appropriate or not for their training in terms of wall-time.

关键词： deep learning second-order optimization neural networks

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ROCK-CNN: a distributed RockPro64-based Convolutional neural network Cluster for IoT. Verification and Performance Analysis

ROCK-CNN: a Distributed RockPro64-based Convolutional Neural...

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Open Innovations Association (FRUCT), conference of

作者： Rezeda Khaydarova Vladislav Fishchenko Dmitriy Mouromtsev Vladislav Shmatkov Maksim Lapaev ITMO University St.Petersburg Russia “TPP Lab” LTD St.Petersburg Russia

ISBN: (数字)9789526924427

ISBN: (纸本)9781728142579

The paper is dedicated to optimization of machine learning and neural networks applications by replacing common servers with Single Board Computer (SBC) clusters to minimize mounting and service expenses, simplify node mounting process and organize parallel computing in IoT applications. Authors focus on former experience of using distributed computing, mainly, light-weight and cost-optimized SBCs to classify use- cases, then, choose an appropriate hardware platform enabling sufficient data processing and easy hot-replacement of nodes. This task requires organizing an efficient software architecture to make use of advantages of SBCs. A comparison for various SBSs is presented. Authors suggest their formerly-designed architecture with changes allowing using it for neural network applications. Authors pay attention to thorough parameter examination based on numerous tests. Parameter timelines are presented. The paper describes a number of test-cases to validate the efficiency of suggested architecture based on common use-cases. Performance analysis and cluster scalability potential estimation are conducted as well to estimate an efficient number of nodes required for future tasks.

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Prediction of Solar Irradiance Based on BP neural network with Wavelet Decomposition 2019

Prediction of Solar Irradiance Based on BP Neural Network wi...

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Proceedings of the 2nd International conference on Information Technologies and Electrical Engineering

作者： Bo Xu Min Lei Yifei Hua Po Wang College of Electrical and Information Engineering Hunan University of Technology Zhuzhou China Hunan University of Technology Zhuzhou China

ISBN: (纸本)9781450372930

To improve the precision of solar irradiation (SI) prediction, this paper presents an SI prediction method based on BP neural network (BPNN) with wavelet decomposition (WD). Firstly, analyze data by wavelet, and WD applied to train input set, training target set, and testing target set. Then combined with BPNN to train, predict, and normalize the decomposed data. Finally, the anticipated results of the two methods are output as predicted values. The prediction results show that the proposed manner is consistent with the expected value curve when predicting SI, which improves the prediction accuracy and provides a basis for SI prediction of photovoltaic power plants.

关键词： Wavelet Decomposition distributed Energy Normalized processing Solar Irradiance Prediction BP neural network

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Performance Analysis of Spiking RBM with Measurement-Based Phase Change Memory Model 1

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26th International conference on neural Information processing (ICONIP) of the Asia-Pacific-neural-network-Society (APNNS)

作者： Ishii, Masatoshi Ito, Megumi Kim, Wanki Kim, SangBum Nomura, Akiyo Okazaki, Atsuya Okazawa, Junka Hosokawa, Kohji BrightSky, Matt Haensch, Wilfried IBM Res Tokyo Tokyo Japan IBM Res TJ Watson Res Ctr New York NY USA Seoul Natl Univ Seoul South Korea

ISBN: (数字)9783030368029

ISBN: (纸本)9783030368029;9783030368012

Phase change memory (PCM) is arguably one of the most promising non-volatile memories which can be used in neuromorphic applications. When we use PCM as analog synaptic elements, non-ideality impact should be carefully taken into account. In this paper, we investigate the impact of such nonideality items as nonlinear weight update and resistance drift on training accuracy in a spiking restricted Boltzmann machine (RBM). In addition to ideal PCM model, actual measurement-based PCM characteristics is used for this study. The resistance drift affects training accuracy, especially in a widely distributed read interval time caused by spatial and temporal sparse spike activities during training in spiking RBM. Our simulation results show that the training accuracy worsens with the increase of the weight-update nonlinearity or the resistance-drift coefficient. However, the results also suggest we can expect more than 8.95% improvement in training accuracy. This potential improvement will be possible if we use linearity-improved confined PCM cells whose median resistance drift coefficient is 0.005, compared to the resistance drift coefficient of more than 0.02 in typical existing PCM cells.

关键词： Spiking neural network Phase change memory Resistance drift

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Proceedings - 2019 IEEE Intl Conf on Parallel and distributed processing with Applications, Big Data and Cloud Computing, Sustainable Computing and Communications, Social Computing and networking, ISPA/BDCloud/SustainCom/SocialCom 2019

Proceedings - 2019 IEEE Intl Conf on Parallel and Distribute...

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17th IEEE International conference on Parallel and distributed processing with Applications, 9th IEEE International conference on Big Data and Cloud Computing, 9th IEEE International conference on Sustainable Computing and Communications, 12th IEEE International conference on Social Computing and networking, ISPA/BDCloud/SustainCom/SocialCom 2019

ISBN: (纸本)9781728143286

The proceedings contain 232 papers. The topics discussed include: convolutional neural network-based approach for citrus diseases recognition;using temporal conceptual graphs and neural networks for big data-based attack scenarios reconstruction;taxi demand prediction with LSTM-based combination model;counting attention based on classification confidence for visual question answering;an optimization method of WebP images lossy compression algorithm for FPGAs;self-adaptive address mapping mechanism for access pattern awareness on DRAM;a high-performance self-learning antelopes migration algorithm (SAMA) for global optimization;and using resource use data and system logs for HPC system error propagation and recovery diagnosis.

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neural third-octave graphic equalizer 22

Neural third-octave graphic equalizer

引用

22nd International conference on Digital Audio Effects, DAFx 2019

作者： Rämö, Jussi Välimäki, Vesa Acoustics Lab Dept. of Signal Processing and Acoustics Aalto University Espoo Finland

This paper proposes to speed up the design of a third-order graphic equalizer by training a neural network to imitate its gain optimization. Instead of using the neural network to learn to design the graphic equalizer by optimizing its magnitude response, we present the network only with example command gains and the corresponding optimized gains, which are obtained with a previously proposed least-squares-based method. We presented this idea recently for the octave graphic equalizer with 10 band filters and extend it here to the third-octave case. Instead of a network with a single hidden layer, which we previously used, this task appears to require two hidden layers. This paper shows that good results can be reached with a neural network having 62 and 31 units in the first and the second hidden layer, respectively. After the training, the resulting network can quickly and accurately design a third-order graphic equalizer with a maximum error of 1.2 dB. The computing of the filter gains is over 350 times faster with the neural network than with the original optimization method. The method is easy to apply, and may thus lead to widespread use of accurate digital graphic equalizers. Copyright: © 2019 Jussi Rämö et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 Unported License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

关键词： Equalizers

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A Power Efficient neural network Implementation on Heterogeneous FPGA and GPU Devices 20

A Power Efficient Neural Network Implementation on Heterogen...

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20th IEEE International conference on Information Reuse and Integration for Data Science (IEEE IRI)

作者： Tu, Yuexuan Sadiq, Saad Tao, Yudong Shyu, Mei-Ling Chen, Shu-Ching Univ Miami Dept Elect & Comp Engn Coral Gables FL 33124 USA Florida Int Univ Sch Comp & Informat Sci Miami FL 33199 USA

ISBN: (纸本)9781728113371

Deep neural networks (DNNs) have seen tremendous industrial successes in various applications, including image recognition, machine translation, audio processing, etc. However, they require massive amounts of computations and take a lot of time to process. This quickly becomes a problem in mobile and handheld devices where real-time multimedia applications such as face detection, disaster management, and CCTV require lightweight, fast, and effective computing solutions. The objective of this project is to utilize specialized devices such as Field Programmable Gate Arrays (FPGAs) and Graphics processing Units (GPUs) in a heterogeneous computing environment to accelerate the deep learning computations with the constraints of power efficiency. We investigate an efficient DNN implementation and make use of FPGA for fully-connected layer and GPU for floating-point operations. This requires the deep neural network architecture to be implemented in a model parallelism system where the DNN model is broken down and processed in a distributed fashion. The proposed heterogeneous framework idea is implemented using an Nvidia TX2 GPU and a Xilinx Artix-7 FPGA. Experimental results indicate that the proposed framework can achieve faster computation and much lower power consumption.

关键词： FPGA GPU Heterogeneous Computing Low-Powered Devices

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Stabilizer of SMPC Throughput using neural networks

Stabilizer of SMPC Throughput using Neural Networks

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International conference on distributed Computing Systems

作者： Michiko Harayama Noboru Miyagawa Gifu University Gifu Japan Div. Natural science and Technology Graduate School Gifu University Gifu Japan

ISBN: (数字)9781728170022

ISBN: (纸本)9781728170039

In view of the successful application of deep learning mainly in the field of image recognition, applications of deep learning in the fields of communication and computer networks are being explored. In these fields, systems that follow proper theoretical calculations and procedures are set up. However, due to the large amount of data to be processed, proper processing takes time, and the aspect that deviates from the theory due to the inclusion of uncertain disturbances sometimes appears. Therefore, deep learning or nonlinear approximation by neural networks may be useful in some cases. The communication method SMPC we have studied measures the throughput by a set of packets called train at the destination node and feeds it back to the source node. By comparing it with the transmission rate, the source node detects congestion on the transmission route and adjusts the packet transmission interval. However, there is a problem that the throughput fluctuates during packets pass through the route, and if it is fed back directly, the transmission rate fluctuates significantly, which causes the fluctuation of the throughput larger and the average throughput even lower. It is not desirable to change the transmission rate unnecessarily. In this study, we tried to stabilize the transmission rate by incorporating prediction and learning by neural network. Optimal transmission rates were predicted by a neural network from the throughput data measured by the destination node, and the results are learned by another neural network to generate a stabilizer. A simple moving average method and a stabilizer using three types of neural networks, MLP, RNN, and LSTM, were built into the transmission controller of the SMPC. It was shown that not only can the fluctuation be reduced, but also the average throughput improves. It is shown that deep learning can be used as a device to predict and output stable values from data with complicated time fluctuations that are difficult to analyze.

关键词： Deep learning Fluctuations Recurrent neural networks Image recognition neural networks Throughput Feeds

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