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Efficient Dual Batch Size Deep Learning for distributed Parameter Server Systems 46

Efficient Dual Batch Size Deep Learning for Distributed Para...

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46th Annual IEEE-Computer-Society International Computers, Software, and Applications conference (COMPSAC) - Computers, Software, and Applications in an Uncertain World

作者： Lu, Kuan-Wei Liu, Pangfeng Hong, Ding-Yong Wu, Jan-Jan Natl Taiwan Univ Grad Inst Networking & Multimedia Taipei Taiwan Natl Taiwan Univ Dept Comp Sci & Informat Engn Taipei Taiwan Acad Sinica Inst Informat Sci Taipei Taiwan

ISBN: (纸本)9781665488105

distributed machine learning is essential for applying deep learning models with many data and parameters. Current researches on distributed machine learning focus on using more hardware devices powerful computing units for fast training. Consequently, the model training prefers a larger batch size to accelerate the training speed. However, the large batch training often suffers from poor accuracy due to poor generalization ability. Researchers have come up with many sophisticated methods to address this accuracy issue due to large batch sizes. These methods usually have complex mechanisms, thus making training more difficult. In addition, powerful training hardware for large batch sizes is expensive, and not all researchers can afford it. We propose a dual batch size learning scheme to address the batch size issue. We use the maximum batch size of our hardware for maximum training efficiency we can afford. In addition, we introduce a smaller batch size during the training to improve the model generalization ability. Using two different batch sizes in the same training simultaneously will reduce the testing loss and obtain a good generalization ability, with only a slight increase in the training time. We implement our dual batch size learning scheme and conduct experiments. By increasing 5% of the training time, we can reduce the loss from 1.429 to 1.246 in some cases. In addition, by appropriately adjusting the percentage of large and small batch sizes, we can increase the accuracy by 2.8% in some cases. With the additional 10% increase in training time, we can reduce the loss from 1.429 to 1.193. And after moderately adjusting the number of large batches and small batches used by GPUs, the accuracy can increase by 2.9%. Using two different batch sizes in the same training introduces two complications. First, the data processing speeds for two different batch sizes are different, so we must assign the data proportionally to maximize the overall processing speed. I

关键词： deep neural networks batch size distributed learning parameter server

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ST-PINN: A Self-Training Physics-Informed neural network for Partial Differential Equations

ST-PINN: A Self-Training Physics-Informed Neural Network for...

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International Joint conference on neural networks (IJCNN)

作者： Junjun Yan Xinhai Chen Zhichao Wang Enqiang Zhoui Jie Liu Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Changsha China

Partial differential equations (PDEs) are an essential computational kernel in physics and engineering. With the advance of deep learning, physics-informed neural networks (PINNs), as a mesh-free method, have shown great potential for fast PDE solving in various applications. To address the issue of low accuracy and convergence problems of existing PINNs, we propose a self-training physics-informed neural network, ST-PINN. Specifically, ST-PINN introduces a pseudo label based self-learning algorithm during training. It employs governing equation as the pseudo-labeled evaluation index and selects the highest confidence examples from the sample points to attach the pseudo labels. To our best knowledge, we are the first to incorporate a self-training mechanism into physics-informed learning. We conduct experiments on five PDE problems in different fields and scenarios. The results demonstrate that the proposed method allows the network to learn more physical information and benefit convergence. The ST-PINN outperforms existing physics-informed neural network methods and improves the accuracy by a factor of 1.33x-2.54x.

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Neuromorphic Computing in Autonomous distributed Grid Control

Neuromorphic Computing in Autonomous Distributed Grid Contro...

引用

AI, Science, Engineering, and Technology (AIxSET), conference on

作者： James Obert Rodrigo D. Trevizan Adrian Chavez Sandia National Labs Albuquerque NM

ISBN: (数字)9798350390995

ISBN: (纸本)9798350391008

Due to the distributed nature of the electrical grid, intelligent, timely control of critical components such as volt-age regulators, capacitor banks and switches in a highly dynamic environment is extremely challenging. Modern day localized controllers in a distribution system such as remote ter-minal units (RTUs) and programmable logic units (PLCs) are often housed in a substation with multiple connections to several critical controls and are thus good candidates for hosting advanced localized control via deep reinforcement learning (DRL) or evolutionary learning. Additionally, it is feasible to greatly reduce the computational throughput and provide fine-grained control measurement and control using a spiking neural network (SNN) neural processing unit (NPU) architecture. In this paper we explore the use of a hybrid grid control algorithm composed of an optimized genetic algorithm (GA) and SNN controller which acquires optimal distribution system voltage control policies while a system is experiencing a cyber adver-sary's injected control noise. We discuss the computational advantages of the GA-SNN hybrid algorithm in the fine-grained autonomous control of a grid's bus voltages using a commercially available neuromorphic system on chip system (NSoC) with a mean rated power consumption of 1 mW and observed mean prediction time of 4 msecs per inference.

关键词： Substations Regulators Power demand Process control Spiking neural networks Throughput Prediction algorithms System-on-chip Voltage control Genetic algorithms

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Pipelined Model Parallelism: Complexity Results and Memory Considerations 27th

Pipelined Model Parallelism: Complexity Results and Memory C...

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27th International European conference on Parallel and distributed Computing (Euro-Par)

作者： Beaumont, Olivier Eyraud-Dubois, Lionel Shilova, Alena Univ Bordeaux Inria Bordeaux Sud Ouest Bordeaux France

ISBN: (纸本)9783030856656;9783030856649

The training phase in Deep neural networks has become an important source of computing resource usage and the resulting volume of computation makes it crucial to perform efficiently on parallel architectures. Data parallelism is the most widely used method, but it requires to replicate the network weights on all processors, and to perform collective communications of the network weights. In this context, model parallelism is an attractive alternative, in which the different layers of the network are distributed over the computing processors. Indeed, it is expected to better distribute weights (to cope with memory problems) and it eliminates the need for large collective communications since only forward activations are communicated. However, to be efficient, it must be combined with pipelining, which in turn induces new memory costs. In this paper, our goal is to formalize pipelined model parallelism as a scheduling problem, to establish its complexity, and to analyze the importance of the assumptions of contiguity and 1-periodicity, implicitly made in practical solutions such as PipeDream.

关键词： Deep neural networks

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Venus: A Versatile Deep neural network Accelerator Architecture Design for Multiple Applications

Venus: A Versatile Deep Neural Network Accelerator Architect...

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Design Automation conference

作者： Jiaqi Yang Hao Zheng Ahmed Louri Electrical and Computer Engineering George Washington University Washington DC USA Electrical and Computer Engineering University of Central Florida Orlando Florida USA

Deep neural network (DNN) applications are pervasive. However as demands for these applications continue to increase, so is the challenges for designing flexible and scalable architectures for multi-application implementation. Such accelerators require innovative architecture with flexible network-on-Chips (NoCs), parallelism exploitation, and better on-chip memory organization to adequately support the diverse computation, memory, and communication needs. In this paper, we propose Venus, a versatile DNN accelerator design that can provide efficient communication and computation support for multi-applications. Venus is a tile-based architecture with a distributed buffer where each tile consists of an array of processing elements (PEs) and a portion of the distributed buffer. The other salient feature of Venus is a flexible network-on-Chip (NoC) that can dynamically adapt to the communication needs of various running applications thus maximizing data reuse, reducing DRAM accesses, and supporting multiple dataflows with an overall aim of better execution time and better energy efficiency. Simulation results show that our proposed Venus design outperforms state-of-art accelerators (NVDLA [1], ShiDianNao [2], Eyeriss [3], Planaria [4], Simba [5]).

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A Lightweight Generative Adversarial network for Artistic Style Transfer

A Lightweight Generative Adversarial Network for Artistic St...

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International conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Ping'an Qiao Tongtong He College of Computer Science Xi'an University of Posts and Telecommunications Xi'an China

ISBN: (数字)9798350356328

ISBN: (纸本)9798350356335

Currently, image style transfer is one of the hotspots in image processing. Generative adversarial networks have been successfully applied in many fields by virtue of their advantages, and the technology of image style transfer has also seen improvements. However, there are still some problems with the existing artistic style transfer models. For example, the generated images will contain noisy textures, the style and color effects are not ideal, and the model has many parameters, which is easy to cause model breakdown and unstable training. To overcome these problems, an image transfer method that improves the cycleconsistent generative adversarial network (CycleGAN) is presented in this paper. According to its structural characteristics, distributed shift convolution (DSConv) is introduced through the quantized neural network, and the reverse residual block is used to optimize the generator network structure, which can greatly reduce the calculation amount of the model while also improving the model's perception ability and accuracy and the quality of the generated images. Experiments show that the proposed approach can improve the content details of the generated image andthe color effect of the style texture, significantly reduces the number of model parameters, reduces the computational cost, improves the transfer rate, upgrading the model performance, and has strong style transfer capabilities.

关键词： Training Image color analysis Convolution Computational modeling neural networks Generative adversarial networks Knowledge discovery Generators Noise measurement Computational complexity

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Convolutional neural networks for solving problems of signal processing from segmental distributed fiber optic measuring networks 13

Convolutional neural networks for solving problems of signal...

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13th Multiconference on Control Problems, MCCP 2020

作者： Kulchin, Yu.N. Kim, A.Yu. Iacp Feb Ras Vladivostok Russia

The work is devoted to the problem of critical situations recognition in real-time and localization of increased acoustic vibration in critical objects, based on the signals from segmental distributed fiber-optic measuring networks (DFOMN). To solve the problem, an architecture is proposed, and a model of a situational approach to using the Convolutional neural network as an effective classification method for processing large data arrays of the DFOMN is developed. © Published under licence by IOP Publishing Ltd.

关键词： Convolution

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Efficient Diabetes Detection using Hybrid Machine Learning Model

Efficient Diabetes Detection using Hybrid Machine Learning M...

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distributed Systems, Computer networks and Cybersecurity (ICDSCNC), International conference on

作者： A S Gowri P Jose Karthik S M S Berlin Shaheema K M Karuppasway R. Balamurugan Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department for Applied Research The Gandhigram Rural Institute (Deemed to be University) Gandhigram India Department of Artificial Intelligence and Data Science Stella Mary’s College of Engineering Azhikal India Department of Mathematics and Statistics Bharath Institute of Higher Education and Research Chennai India Department of Information Technology DMI College of Engineering Chennai India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Diabetes is a metabolic disease that affects a large number of the global population and is incurable. The primary causes of death symptoms are kidney failure, heart attacks, strokes, and blindness. In this paper Principal component analysis used for pre-processing the data to extract features and The Efficient Hybrid Machine Learning Model Convolution neural network-Support Vector Machine is used to categorize the Pima Indian dataset. CNN and non-linear SVM used in a hybrid model to classify the diabetes data from the Pima Indians. To identify positive classes of pima Indians diabetes, the HCNN-SVM extract the features from the original data expression and learns diabetes data through a novel convolution network environment to the SVM classifier with RBF function and achieve the accuracy as 98,04%.

关键词： Support vector machines Accuracy Convolution Machine learning Feature extraction Data models Diabetes Data mining Convolutional neural networks Principal component analysis

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Botnet Attacks Detection using Image processing Approach: Sequential neural network and Feature Selection

Botnet Attacks Detection using Image Processing Approach: Se...

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International conference on Computing and networking Technology (ICCNT)

作者： Taufiq Odhi Dwi Putra Tohari Ahmad Department of Informatics Institut Teknologi Sepuluh Nopember Surabaya Indonesia

Botnets are networks of compromised computers that are controlled by an attacker for malicious purposes such as spamming attacks, DDoS, or data theft. Botnet attacks detection is a challenging task due to their distributed and covert nature. Botnet attacks are a major security concern for organizations and individuals alike. In this paper we proposed a botnet attacks detection using image processing approach. We propose an AI model architecture using Sequential neural network and feature selection. There are two methods for converting raw data into image data. First method using predefined categorize for choosing features. The second method uses Pearson correlation value between features in dataset and the label. Predefined categorized features have successfully increased the performance of proposed AI model for botnet attacks detection as well as Pearson correlation value.

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Leveraging Ultra-Law-Power Wearables Using distributed neural networks

Leveraging Ultra-Law-Power Wearables Using Distributed Neura...

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International Workshop on Wearable and Implantable Body Sensor networks (BSN)

作者： Meghna Roy Chowdhury Archisman Ghosh Md Faizul Bari Shreyas Sen

ISBN: (数字)9798331530143

ISBN: (纸本)9798331530150

Traditional deep learning models incur high computational overhead (approximately 1-30W) and are unsuitable for Ultra-Low-Power (ULP) wearable systems like smart glasses. In contrast, TinyML architectures are power-efficient but less accurate. We propose distributing a neural network (NN) between a ULP wearable node and a resource-rich hub to maintain high accuracy and low power consumption for applications like human-machine vision. Output features from the node are transmitted to the hub via low-power communication, where the remaining network runs on traditional GPUs. We introduce a Figure of Merit (FoM) to determine the optimal NN distribution point and a customized multiplier unit for ULP operation. Achieving ULP of 284μW and 9mW for different Autoencoder (AE) networks, our approach is 700× lower in power consumption compared to traditional GPU implementation.

关键词： Power demand Accuracy Tiny machine learning Scalability Noise reduction Graphics processing units Artificial neural networks Medical services Sensors Smart glasses

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