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A Many-Core Accelerator Design for On-Chip Deep Reinforcement Learning

A Many-Core Accelerator Design for On-Chip Deep Reinforcemen...

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IEEE International conference on Computer-Aided Design

作者： Ying Wang Mengdi Wang Bing Li Huawei Li Xiaowei Li State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing P.R. China Capital Normal University Beijing P.R. China

ISBN: (数字)9781665423243

Deep Reinforcement Learning (DRL) is substantially resource-consuming, and it requires large-scale distributed computing-nodes to learn complicated tasks, like video-game and Go play. This work attempts to down-scale a distributed DRL system into a specialized many-core chip and achieve energy-efficient on-chip DRL. With the customized network-on-Chip that handles the communication of on-chip data and control-signals, we proposed a Synchronous Asynchronous RL Architecture (SARLA) and the according many-core chip that completely avoids the unnecessary data duplication and synchronization activities in multi-node RL systems. In evaluation, the SARLA system achieves considerable energy-efficiency boost over the GPU-based implementations for typical DRL workloads built with OpenAI-gym.

关键词： System-on-chip Computer architecture Reinforcement learning neural networks Servers Parallel processing Training

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distributed LARGE neural network WITH CENTRALIZED EQUIVALENCE

DISTRIBUTED LARGE NEURAL NETWORK WITH CENTRALIZED EQUIVALENC...

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

作者： Liang, Xinyue Javid, Alireza M. Skoglund, Mikael Chatterjee, Saikat KTH Royal Inst Technol Sch Elect Engn Dept Informat Sci & Engn Stockholm Sweden

ISBN: (纸本)9781538646588

In this article, we develop a distributed algorithm for learning a large neural network that is deep and wide. We consider a scenario where the training dataset is not available in a single processing node, but distributed among several nodes. We show that a recently proposed large neural network architecture called progressive learning network (PLN) can be trained in a distributed setup with centralized equivalence. That means we would get the same result if the data be available in a single node. Using a distributed convex optimization method called alternating-direction-method-of-multipliers (ADMM), we perform training of PLN in the distributed setup.

关键词： distributed learning neural networks data parallelism convex optimization

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Efficient Estimation of Ontology Entities distributed Representations 14th

Efficient Estimation of Ontology Entities Distributed Repres...

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14th International conference on Knowledge Management in Organizations (KMO) - Synergistic Role of Knowledge Management in Organization

作者： Benarab, Achref Sun, Jianguo Refoufi, Allaoua Guan, Jian Harbin Engn Univ Coll Comp Sci & Technol Harbin Peoples R China Univ Setif 1 Comp Sci Dept Setif Algeria

ISBN: (纸本)9783030214517;9783030214500

Ontologies have been used as a form of knowledge representation in different fields such as artificial intelligence, semantic web and natural language processing. The success caused by deep learning in recent years as a major upheaval in the field of artificial intelligence depends greatly on the data representation, since these representations can encode different types of hidden syntactic and semantic relationships in data, making their use very common in data science tasks. Ontologies do not escape this trend, applying deep learning techniques in the ontology-engineering field has heightened the need to learn and generate representations of the ontological data, which will allow ontologies to be exploited by such models and algorithms and thus automatizing different ontology-engineering tasks. This paper presents a novel approach for learning low dimensional continuous feature representations for ontology entities based on the semantic embedded in ontologies, using a multi-input feed-forward neural network trained using noise contrastive estimation technique. Semantically similar ontology entities will have relatively close corresponding representations in the projection space. Thus, the relationships between the ontology entities representations mirrors exactly the semantic relations between the corresponding entities in the source ontology.

关键词： Ontology entities distributed representations Continuous vector representations Feature representation Concept embeddings neural networks

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5th International conference on Information Management and Big Data, SIMBig 2018

5th International Conference on Information Management and B...

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5th International conference on Information Management and Big Data, SIMBig 2018

ISBN: (纸本)9783030116798

The proceedings contain 34 papers. The special focus in this conference is on Information Management and Big Data. The topics include: Aerial scene classification and information retrieval via fast kernel based fuzzy C-means clustering;a case study of library data management: A new method to analyze borrowing behavior;sparkmach: A distributed data processing system based on automated machine learning for big data;deep dive into authorship verification of email messages with convolutional neural network;monitoring of air quality with low-cost electrochemical sensors and the use of artificial neural networks for the atmospheric pollutants concentration levels prediction;data mining algorithms for risk detection in bank loans;DETECTOR: Automatic detection system for terrorist attack trajectories;car monitoring system in apartments’ Garages by small autonomous car using deep learning;a framework for analytical approaches to combine interpretable models;which is the tallest building in Europe? Representing and reasoning about knowledge;processing Quechua and Guarani historical texts query expansion at character and word level for information retrieval;Topic modeling applied to business research: A latent dirichlet allocation (LDA)-based classification for organization studies;using neural network for identifying clickbaits in online news media;spanish named entity recognition in the biomedical domain;ontology modeling of the Estonian traffic act for self-driving buses;thought off-line sanitization methods for bank transactions;big data for development: An approach as a state government capacity in the countries;towards real-time automatic stress detection for office workplaces;soTesTeR: Software testing techniques’ recommender system using a collaborative approach;crowdsourcing high-quality structured data.

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A Framework for distributed Representations of Domain Embedding 38

A Framework for Distributed Representations of Domain Embedd...

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38th Chinese Control conference (CCC)

作者： Hou, Yuqing Jin, Feng Zhao, Baicheng Zhang, Wei Beijing Inst Technol Acad Automat Beijing 100081 Peoples R China

ISBN: (纸本)9789881563972

In many information retrieval and natural language processing tasks, it is common to use pertained word embedding to alleviate training difficulty. This motivate us to learn low-dimension representations for all domains on the Internet. There are hundreds of millions of domains on the Internet, majority of the domains attribute to only one or two specific field. Representing these property of domains in a low-dimensional and interpretable space attribute to many tasks, such as domain recommendation, domain classification, web search and et al. In this paper, we propose a novel algorithm named domain embedding, an unsupervised model which learns a fixed length representation for each domain. Experimental results show the superior performance of the proposed method.

关键词： Domain Embedding neural network Domain Classification

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Construction of Big Data Monitoring Platform for Teaching Quality under Intelligent Education

Construction of Big Data Monitoring Platform for Teaching Qu...

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International Wireless Communications and Mobile Computing conference, IWCMC

作者： Fang Qin Weijia Zeng Lin Li Rong Zhao School of Digital Technology Dalian University of Science and Technology Dalian China

ISBN: (数字)9781728131290

ISBN: (纸本)9781728131306

To a great extent, the quality of teaching determines the level of trained talents. Now it has entered the era of intelligent education, coupled with the rapid development of the Internet, has produced a large number of teaching data, which also makes the monitoring and evaluation of teaching quality become particularly difficult. In view of the above problems, this paper proposes the construction of big data monitoring platform for teaching quality under intelligent education. In this paper, the OPC UA unified architecture is used for communication between devices, and the information configuration is based on the spring boot framework to achieve data collection. Then, data processing is based on GRU neural network, and spark distributed computing framework is used to improve the efficiency of data operation. Finally, the monitoring effect is realized by constructing the evaluation system.

关键词： Education Big Data Monitoring Sparks distributed computing Computer architecture

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Deepfakes Detection with Automatic Face Weighting

Deepfakes Detection with Automatic Face Weighting

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IEEE Computer Society conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Daniel Mas Montserrat Hanxiang Hao S. K. Yarlagadda Sriram Baireddy Ruiting Shao János Horváth Emily Bartusiak Justin Yang David Güera Fengqing Zhu Edward J. Delp Video and Image Processing Laboratory (VIPER) School of Electrical Engineering Purdue University West Lafayette Indiana USA

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Altered and manipulated multimedia is increasingly present and widely distributed via social media platforms. Advanced video manipulation tools enable the generation of highly realistic-looking altered multimedia. While many methods have been presented to detect manipulations, most of them fail when evaluated with data outside of the datasets used in research environments. In order to address this problem, the Deepfake Detection Challenge (DFDC) provides a large dataset of videos containing realistic manipulations and an evaluation system that ensures that methods work quickly and accurately, even when faced with challenging data. In this paper, we introduce a method based on convolutional neural networks (CNNs) and recurrent neural networks (RNNs) that extracts visual and temporal features from faces present in videos to accurately detect manipulations. The method is evaluated with the DFDC dataset, providing competitive results compared to other techniques.

关键词： Face Feature extraction Streaming media Recurrent neural networks Training Tools Social network services

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Islanding detection using bi-directional energy meter in a DFIG based active distribution network

Islanding detection using bi-directional energy meter in a D...

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2019 International conference on Computer, Electrical and Communication Engineering, ICCECE 2019

作者： Verma, Shailesh Dutta, Soham Sadhu, Pradip Kumar Bharata Reddy, M. Jaya Mohanta, Dusmanta Kumar Department of Electrical Engineering Dhanbad Jharkhand India National Institute of Technology Department of Electrical and Electronics Engineering Tiruchirappalli Tamilnadu India Birla Institute of Technology Department of Electrical and Electronics Engineering Mesra Jharkhand India

ISBN: (纸本)9781538666470

With the deregulation of the electrical power market, the distribution architecture has now heavily integrated distributed generation (DG) technologies. The presence of DGs has reformed power grid management leading to concepts such as micro-grid technology. Despite its huge advantages like smoothing power grid operations, improving power quality and managing capricious demand, DGs have limitations like unplanned islanding. Prolonged accidental islanding can seriously damage utility resources and compromise the safety and serviceability. Thus, it's critical to have a protection scheme which enables quick and reliable islanding detection. Though many techniques have been developed recently, they lack either consistency or swiftness. The proposed methodology is based on voltage measurement directly from a bi-directional energy meter and processing of these signals with constant-Q transform (CQT). This technique determines, if the abnormity in a micro-grid is because of any fault or an island operation by using artificial neural network (ANN). The proposed algorithm and its efficiency is demonstrated and justified with MATLAB simulations. Various islanding events as well as non-islanding events in a wind based DG micro-grid is considered for the proposed method. © 2019 IEEE.

关键词： neural networks

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From Orthography to Semantics: a Study of Morphological processing through Deep Learning neural networks

From Orthography to Semantics: a Study of Morphological Proc...

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International Joint conference on neural networks (IJCNN)

作者： Davila, Jaime Morris, Joanna Hampshire Coll Sch Cognit Sci Amherst MA 01002 USA

ISBN: (纸本)9781509060146

This paper presents results of using deep learning neural network techniques to map words, represented at the levels of letters, into semantic, distributed vector representations. We study and compare algorithms that have been proposed as models for human orthographic and morphological processing, such as the two layers symbolic network (AKA Naive Discriminatory Learning network) proposed by Baayen et al [1], and the dual-route approach presented by Grainger and Ziegler [2]. In addition, we study the effect of representing letters as one-hot vectors or via distributed vector representations, much like the natural language processing field has done for words. Experiment results show a better performance for the dual-route algorithm using distributed letter representations, both in terms of accuracy and speed. Our results are obtained with training sets in the tens of thousands of words, as opposed to the hundreds of thousands of words used elsewhere in the literature. These results point to the feasibility of taking advantage of morpho-orthographic patterns in order to assist with word processing in natural language processing tasks.

关键词： Semantics neural networks Training network topology Topology Task analysis Linguistics

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Real-time Crop Classification Using Edge Computing and Deep Learning

Real-time Crop Classification Using Edge Computing and Deep ...

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Consumer Communications and networking conference, CCNC IEEE

作者： Ming Der Yang Hsin Hung Tseng Yu Chun Hsu Wei Chen Tseng National Chung Hsing University Pervasive AI Research (PAIR) Labs Taichung Taiwan National Chung Hsing University Taichung Taiwan

ISBN: (数字)9781728138930

ISBN: (纸本)9781728138947

In recent years, edge computing and deep learning have been successfully performed processing and classification tasks in a variety of fields including agriculture. Therefore, this research aims to use unmanned aerial vehicle (UAV) for agriculture applications with integrating edge computing and deep learning techniques. This research experiment was carried out in the NCHU Experimental Farm. The DJI Matrice 100 drone with ASUS Tinker Board S embedded system, which connects a Logitech C925e webcam to capture images are used in this study. The ASUS Tinker Board S runs a folder monitoring program and sends images over the 4G LTE network to the backend server whenever new images are captured and stored. The backend server runs a pre-trained image semantic segmentation model and provides image inference service. The image inference results with the associated segmented image will be sent to the mobile device of the drone controller, and a dynamic flight control action can be triggered. The image semantic segmentation model adopts SegNet network architecture. For a comparison purpose, another network architecture, FCN-AlexNet, was also trained and validated. The preliminary results show SegNet outperformed FCN-AlexNet in image semantic segmentation tasks in terms of the evaluation between training and validation. The average inference speed of the semantic image segmentation model is 0.7s with segmentation identification accuracy is 89%. The promising results shed light on many agriculture applications, such as crop growth condition assessment, fertilizer management, and yield prediction. Additionally, this research provides possible solutions for the labor shortage issue of agriculture which is a common challenge in an aging community like Taiwan and many countries worldwide.

关键词： 4G mobile communication autonomous aerial vehicles control engineering computing convolutional neural nets crops distributed processing image capture image classification image segmentation learning (artificial intelligence) Long Term Evolution 4G mobile communication control engineering computing Long Term Evolution image segmentation image capture distributed processing Image classification images crops drone honey bees agriculture Image Imagery (Psychotherapy) Semantics

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