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arXiv 2018年

作者： Xu, Kele Mi, Haibo Feng, Dawei Wang, Huaimin Chen, Chuan Zheng, Zibin Lan, Xu National Key Laboratory of Parallel and Distributed Processing Changsha China College of Computer National University of Defense Technology Changsha China School of Data and Computer Science Sun Yat-Sen University Guangzhou China Queen Mary University of London London United Kingdom

Valuable training data is often owned by independent organizations and located in multiple data centers. Most deep learning approaches require to centralize the multi-datacenter data for performance purpose. In practice, however, it is often infeasible to transfer all data to a centralized data center due to not only bandwidth limitation but also the constraints of privacy regulations. Model averaging is a conventional choice for data parallelized training, but its ineffectiveness is claimed by previous studies as deep neural networks are often non-convex. In this paper, we argue that model averaging can be effective in the decentralized environment by using two strategies, namely, the cyclical learning rate and the increased number of epochs for local model training. With the two strategies, we show that model averaging can provide competitive performance in the decentralized mode compared to the data-centralized one. In a practical environment with multiple data centers, we conduct extensive experiments using state-of-the-art deep network architectures on different types of data. Results demonstrate the effectiveness and robustness of the proposed method. Copyright © 2018, The Authors. All rights reserved.

关键词： Network architecture

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Sample dropout for audio scene classification using multi-scale dense connected convolutional neural network

arXiv

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arXiv 2018年

作者： Feng, Dawei Xu, Kele Mi, Haibo Liao, Feifan Zhou, Yan Science and Technology on Parallel and Distributed Laboratory School of Computer National University of Defense Technology Changsha410073 China School of Information and Communication National University of Defense Technology Wuhan430010 China

Acoustic scene classification is an intricate problem for a machine. As an emerging field of research, deep Convolutional Neural Networks (CNN) achieve convincing results. In this paper, we explore the use of multi-scale Dense connected convolutional neural network (DenseNet) for the classification task, with the goal to improve the clas-sification performance as multi-scale features can be extracted from the time-frequency representation of the audio signal. On the other hand, most of previous CNN-based audio scene classification approaches aim to improve the classification accuracy, by employing different regularization techniques, such as the dropout of hidden units and data augmentation, to reduce overfitting. It is widely known that outliers in the training set have a high negative inuence on the trained model, and culling the outliers may improve the classification performance, while it is often under-explored in previous studies. In this paper, inspired by the silence removal in the speech signal processing, a novel sample dropout approach is proposed, which aims to remove outliers in the training dataset. Using the DCASE 2017 audio scene classification datasets, the experimental results demonstrates the proposed multi-scale DenseNet providing a su-perior performance than the traditional single-scale DenseNet, while the sample dropout method can further improve the classification robustness of multi-scale DenseNet. Copyright © 2018, The Authors. All rights reserved.

关键词： Statistics

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Determinants of pull-based development in the context of continuous integration

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Science China(Information Sciences) 2016年第8期59卷 53-66页

作者： Yue YU Gang YIN Tao WANG Cheng YANG Huaimin WANG College of Computer National University of Defense Technology National Laboratory for Parallel and Distributed Processing

The pull-based development model, widely used in distributed software teams on open source communities, can efficiently gather the wisdom from crowds. Instead of sharing access to a central repository,contributors create a fork, update it locally, and request to have their changes merged back, i.e., submit a pull-request. On the one hand, this model lowers the barrier to entry for potential contributors since anyone can submit pull-requests to any repository, but on the other hand it also increases the burden on integrators, who are responsible for assessing the proposed patches and integrating the suitable changes into the central repository. The role of integrators in pull-based development is crucial. They must not only ensure that pull-requests should meet the project’s quality standards before being accepted, but also finish the evaluations in a timely manner. To keep up with the volume of incoming pull-requests, continuous integration(CI) is widely adopted to automatically build and test every pull-request at the time of submission. CI provides extra evidences relating to the quality of pull-requests, which would help integrators to make final decision(i.e., accept or reject). In this paper, we present a quantitative study that tries to discover which factors affect the process of pull-based development model, including acceptance and latency in the context of CI. Using regression modeling on data extracted from a sample of Git Hub projects deploying the Travis-CI service, we find that the evaluation process is a complex issue, requiring many independent variables to explain adequately. In particular, CI is a dominant factor for the process, which not only has a great influence on the evaluation process per se, but also changes the effects of some traditional predictors.

关键词： pull-request continuous integration Git Hub distributed software development empirical analysis

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Exploiting contention and congestion aware switch allocation in network-on-chips 17

Exploiting contention and congestion aware switch allocation...

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50th ACM Turing Conference - China, ACM TUR-C 2017

作者： Li, Cunlu Dongy, Dezun Liao, Xiangke National Laboratory for Parallel and Distributed Processing Collaborative Innovation Center of High Performance Computing College of Computer National University of Defense Technology Changsha Hunan410073 China

ISBN: (纸本)9781450348737

Network-on-chip system plays an important role to improve the performance of chip multiprocessor systems. As the complexity of the network increases, congestion problem has become the major performance bottleneck and seriously influence the performance of NoCs. Prior works have focused on designing effective routing algorithm based on collecting congestion and contention information to load balance the traffic. However, most prior works do not consider balancing the traffic load during switch allocation. Due to the lack of congestion information in switch allocation stage, switch allocator performs allocation only based on packet requests and thus aggravates the congestion in the ports of switch. In this paper, we propose to add the congestion and contention information into the switch allocation process and design an efficient on-chip switching strategy which utilizes the contention and congestion information to load balance the traffic and achieve efficient switch allocation. To further enhance the performance of our design, we carefully design our switch allocation strategy to balance the trade-off between traffic load balance and the matching efficiency in switch allocation. We evaluate our design under synthetic traffic and trace of parsec benchmarks. Our evaluations show this mechanism achieves optimal latency compared to best previous switch allocation strategies. © 2017 ACM.

关键词： Network-on-chip

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A systematic review of structured sparse learning

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Frontiers of Information Technology & Electronic Engineering 2017年第4期18卷 445-463页

作者： Lin-bo QIAO Bo-feng ZHANG Jin-shu SU Xi-cheng LU College of Computer National University of Defense Technology Changsha 410073 China National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha 410073 China

High-dimensional data arising from diverse scientific research fields and industrial development have led to increased interest in sparse learning due to model parsimony and computational advantage. With the assumption of sparsity, many computational problems can be handled efficiently in practice. Structured sparse learning encodes the structural information of the variables and has been quite successful in numerous research fields. With various types of structures discovered, sorts of structured regularizations have been proposed. These regularizations have greatly improved the efficacy of sparse learning algorithms through the use of specific structural information. In this article, we present a systematic review of structured sparse learning including ideas, formulations, algorithms, and applications. We present these algorithms in the unified framework of minimizing the sum of loss and penalty functions, summarize publicly accessible software implementations, and compare the computational complexity of typical optimization methods to solve structured sparse learning problems. In experiments, we present applications in unsupervised learning, for structured signal recovery and hierarchical image reconstruction, and in supervised learning in the context of a novel graph-guided logistic regression.

关键词： Sparse learning Structured sparse learning Structured regularization

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General audio tagging with ensembling convolutional neural networks and statistical features

arXiv

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arXiv 2018年

作者： Xu, Kele Zhu, Boqing Kong, Qiuqiang Mi, Haibo Ding, Bo Wang, Dezhi Wang, Huaimin National Key Laboratory of Parallel and Distributed Processing Changsha China College of Computer National University of Defense Technology Changsha China University of Surrey United Kingdom College of Meteorology and Oceanography National Univ. of Defense Tech Changsha China

Audio tagging aims to infer descriptive labels from audio clips. Audio tagging is challenging due to the limited size of data and noisy labels. In this paper, we describe our solution for the DCASE 2018 Task 2 general audio tagging challenge. The contributions of our solution include: We investigated a variety of convolutional neural network architectures to solve the audio tagging task. Statistical features are applied to capture statistical patterns of audio features to improve the classification performance. Ensemble learning is applied to ensemble the outputs from the deep classifiers to utilize complementary information. a sample reweight strategy is employed for ensemble training to address the noisy label problem. Our system achieves a mean average precision (mAP@3) of 0.958, outperforming the baseline system of 0.704. Our system ranked the 1st and 4th out of 558 submissions in the public and private leaderboard of DCASE 2018 Task 2 challenge. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep neural networks

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A Review of Indoor-Outdoor Scene Classification

A Review of Indoor-Outdoor Scene Classification

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2017 2nd International Conference on Control, Automation and Artificial Intelligence(CAAI2017)

作者： Zhehang Tong Dianxi Shi Bingzheng Yan Jing Wei National Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology

ISBN: (纸本)9781510845541

Indoor-Outdoor scene classification problem have been proposed for almost 20 years and widely applied to general scene classification, image retrieval, image processing and robot application. But there is no consensus on one particular scene classification technique that can solve the Indoor-Outdoor scene classification problem perfectly. As larger image dataset has been developed and machine learning technology especially deep learning based methods achieve remarkable performance in computer vision, we aim to provide guidance and direction for researchers to tackle the Indoor-Outdoor scene classification problem with more powerful and robust solution through concluding the Indoor-Outdoor scene classification approaches which have been proposed in last 20 years. In this paper, we review the Indoor-Outdoor scene classification including feature extraction, classifier and related dataset. Their advantages and disadvantages are discussed. At last we conclude some challenging problems remain unsolved and propose some potential solutions.

关键词： indoor-outdoor scene classification computer vision

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Growing construction and adaptive evolution of complex software systems

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Science China(Information Sciences) 2016年第5期59卷 5-7页

作者： Huaimin WANG Bo DING National Key Laboratory of Parallel and Distributed Processing College of ComputerNational University of Defense Technology

distributed software systems are becoming more and more complex *** is easy to find a huge amount of computing nodes in a nationwide or global information *** example,We Chat(Wei Xin),a well-known mobile application in China,has reached a record of 650 million monthly active users in the third quarter of *** the same time,researchers are starting to talk about software systems which have billions of lines of codes[1]or can last one hundred years.

关键词： Growing construction and adaptive evolution of complex software systems

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Fine-grained checkpoint based on non-volatile memory

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Frontiers of Information Technology & Electronic Engineering 2017年第2期18卷 220-234页

作者： Wen-zhe ZHANG Kai LU Mikel LUJAN Xiao-ping WANG Xu ZHOU Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha 410072 China School of Computer The University of Manchester Manchester M13 9PL UK

New non-volatile memory （e.g., phase-change memory） provides fast access, large capacity, byteaddressability, and non-volatility features. These features, fast-byte-persistency, will bring new opportunities to fault tolerance. We propose a fine-grained checkpoint based on non-volatile memory. We extend the current virtual memory manager to manage non-volatile memory, and design a persistent heap with support for fast allocation and checkpointing of persistent objects. To achieve a fine-grained checkpoint, we scatter objects across virtual pages and rely on hardware page-protection to monitor the modifications. In our system, two objects in different virtual pages may reside on the same physical page. Modifying one object would not interfere with the other object. This allows us to monitor and checkpoint objects smaller than 4096 bytes in a fine-grained way. Compared with previous page-grained based checkpoint mechanisms, our new checkpoint method can greatly reduce the data copied at checkpoint time and better leverage the limited bandwidth of non-volatile memory.

关键词： Non-volatile memory Byte-persistency Persistent heap Fine-grained checkpoint

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Single event upset induced by single event double transient and its well-structure dependency in 65-nm bulk CMOS technology

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Science China(Information Sciences) 2016年第4期59卷 152-159页

作者： Pengcheng HUANG Shuming CHEN Jianjun CHEN College of Computer National University of Defense Technology National Laboratory for Parallel and Distributed Processing National University of Defense Technology

Single event upset (SEU) is one of the most important origins of soft errors in aerospace *** technology scales down persistently, charge sharing is playing a more and more significant effect on SEU of flip-flop. Charge sharing can often bring about multi-node charge collection in storage nodes and non-storage nodes in a flip-flop. In this paper, multi-node charge collection in flip-flop data input and flip-flop clock signal is investigated by 3D TCAD mixed-mode simulations, and the simulate results indicate that single event double transient (SEDT) in flip-flop data input and flip-flop clock signal can also cause a SEU in flip-flop. This novel mechanism is called the SEDT-induced SEU, and it is also verified by heavy-ion experiment in 65 nm twin-well process. The simulation results also indicate that this mechanism is closely related with the well-structure,and the triple-well structure is more effective to increase the SEU threshold of this mechanism than twin-well structure.

关键词： single event upset (SEU) single event double transient (SEDT) SEDT-induced SEU parasitic bipolar effect (PBE) charge sharing

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