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20th Annual Conference on Computer Engineering and technology, NCCET 2016

作者： Hongguang, Zhang Minxuan, Zhang College of Computer National University of Defense Technology Changsha410073 China National Key Laboratory of Parallel and Distributed Processing National University of Defense Technology Changsha410073 China

ISBN: (纸本)9789811031588

Spin-transfer torque random access memory (STT-RAM) is one of the most promising substitutes for universal main memory and cache due to its excellent scalability, high density and low leakage power. Nevertheless, the current non-volatile STT-RAM cache architecture also has some drawbacks, such as long write latency and high write energy, which limit the application of STTRAM in the top level cache design. To solve these problems, we relax the retention time of STT-RAM to explore its different write performance, and propose a novel STT-RAM L1 cache architecture implemented with volatile STT-RAM as well as its related refresh scheme. The performance of proposed design is the same as SRAM L1 cache while its overall power consumption is only 63.8% of the latter one. © Springer Nature Singapore Pte Ltd. 2016.

关键词： Static random access storage

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Galaxyfly: A novel family of flexible-radix low-diameter topologies for large-scales interconnection networks 16

Galaxyfly: A novel family of flexible-radix low-diameter top...

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30th International Conference on Supercomputing, ICS 2016

作者： Lei, Fei Dong, Dezun Liao, Xiangke Su, Xing Li, Cunlu National Laboratory for Parallel and Distributed Processing Collaborative Innovation Center of High Performance Computing College of Computer National University of Defense Technology Changsha410073 China

ISBN: (纸本)9781450343619

Interconnection network plays an essential role in the architecture of large-scale high performance computing (HPC) systems. In the paper, we construct a novel family of lowdiameter topologies, Galaxyfly, using techniques of algebraic graphs over finite fields. Galaxyfly is guaranteed to retain a small constant diameter while achieving a flexible tradeoff between network scale and bisection bandwidth. Galaxyfly lowers the demands for high radix of network routers and is able to utilize routers with merely moderate radix to build exascale interconnection networks. We present effective congestion-aware routing algorithms for Galaxyfly by exploring its algebraic property. We conduct extensive simulations and analysis to evaluate the performance, cost and power consumption of Galaxyfly against state-of-the-art topologies. The results show that our design achieves better performance than most existing topologies under various routing algorithms and traffic patterns, and is cost-effective to deploy for exascale HPC systems. © 2016 ACM.

关键词： Topology

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Scalability of 3D deterministic particle transport on the Intel MIC architecture

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Nuclear science and Techniques 2015年第5期26卷 88-97页

作者：王庆林刘杰龚春叶邢座程 Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Science and Technology on Space Physics Laboratory

The key to large-scale parallel solutions of deterministic particle transport problem is single-node computation performance. Hence, single-node computation is often parallelized on multi-core or many-core computer architectures. However, the number of on-chip cores grows quickly with the scale-down of feature size in semiconductor technology. In this paper, we present a scalability investigation of one energy group time-independent deterministic discrete ordinates neutron transport in 3D Cartesian geometry(Sweep3D) on Intel's Many Integrated Core(MIC) architecture, which can provide up to 62 cores with four hardware threads per core now and will own up to 72 in the future. The parallel programming model, Open MP, and vector intrinsic functions are used to exploit thread parallelism and vector parallelism for the discrete ordinates method, respectively. The results on a 57-core MIC coprocessor show that the implementation of Sweep3 D on MIC has good scalability in performance. In addition, the application of the Roofline model to assess the implementation and performance comparison between MIC and Tesla K20 C Graphics processing Unit(GPU) are also reported.

关键词：计算机体系结构可扩展性粒子输运三维几何英特尔麦克风离散坐标法计算性能

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Audio Visual Speech Recognition with Multimodal Recurrent Neural Networks

Audio Visual Speech Recognition with Multimodal Recurrent Ne...

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International Joint Conference on Neural Networks

作者： Weijiang Feng Naiyang Guan Yuan Li Xiang Zhang Zhigang Luo Institute of Software College of Computer National University of Defense Technology Changsha Hunan P.R. China 410073 Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology Changsha Hunan P.R. China 410073

ISBN: (纸本)9781509061839

Studies on nowadays human-machine interface have demonstrated that visual information can enhance speech recognition accuracy especially in noisy environments. Deep learning has been widely used to tackle such audio visual speech recognition (AVSR) problem due to its astonishing achievements in both speech recognition and image recognition. Although existing deep learning models succeed to incorporate visual information into speech recognition, none of them simultaneously considers sequential characteristics of both audio and visual modalities. To overcome this deficiency, we proposed a multimodal recurrent neural network (multimodal RNN) model to take into account the sequential characteristics of both audio and visual modalities for AVSR. In particular, multimodal RNN includes three components, i.e., audio part, visual part, and fusion part, where the audio part and visual part capture the sequential characteristics of audio and visual modalities, respectively, and the fusion part combines the outputs of both modalities. Here we modelled the audio modality by using a LSTM RNN, and modelled the visual modality by using a convolutional neural network (CNN) plus a LSTM RNN, and combined both models by a multimodal layer in the fusion part. We validated the effectiveness of the proposed multimodal RNN model on a multi-speaker AVSR benchmark dataset termed AVletters. The experimental results show the performance improvements comparing to the known highest audio visual recognition accuracies on AVletters, and confirm the robustness of our multimodal RNN model.

关键词： Visualization Hidden Markov models Speech recognition Recurrent neural networks Feature extraction Logic gates Data models

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Hardware design of ML algorithm in MIMO-OFDM system

Hardware design of ML algorithm in MIMO-OFDM system

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International Conference on Systems and Informatics (ICSAI)

作者： Suncheng Xiang Minxuan Zhang Zuocheng Xing Cang Liu Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China

ISBN: (纸本)9781509055227

With the development of maritime information technology and computer technology, wireless communication technology in underwater networks and systems has made great progress and achieved wide range of application, which greatly promoted the development of ocean communication. Due to the presence of severe frequency selective fading, fast fading and slow fading, including all kinds of Noise interference and ISI problem under multipath propagation, especially ISI. In the meantime, in order to improve efficiency in underwater communications, a kind of signal detection algorithm is proposed in the case of interference in this article to deal with this problems-Maximum likelihood detection, this algorithm has a good complexity and relatively a high degree of precision in MIMO-OFDM system. So we do a circuit design based on the ML algorithm. The test result shows that this kind of circuit meets the requirements successfully.

关键词： Algorithm design and analysis OFDM Hardware Receiving antennas Integrated circuit modeling Maximum likelihood detection MIMO

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Linearized alternating direction method of multipliers for constrained nonconvex regularized optimization 8

Linearized alternating direction method of multipliers for c...

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8th Asian Conference on Machine Learning, ACML 2016

作者： Qiao, Linbo Zhang, Bofeng Su, Jinshu Lu, Xicheng College of Computer National University of Defense Technology ChangSha410073 China National Laboratory for Parallel and Distributed Processing National University of Defense Tech-nology ChangSha410073 China

In this paper, we consider a wide class of constrained nonconvex regularized minimization problems, where the constraints are linearly constraints. It was reported in the litera- ture that nonconvex regularization usually yields a solution with more desirable sparse structural properties beyond convex ones. However, it is not easy to obtain the proximal mapping associated with nonconvex regularization, due to the imposed linearly constraints. In this paper, the optimization problem with linear constraints is solved by the Linearized Alternating Direction Method of Multipliers (LADMM). Moreover, we present a detailed convergence analysis of the LADMM algorithm for solving nonconvex compositely regular- ized optimization with a large class of nonconvex penalties. Experimental results on several real-world datasets validate the efficacy of the proposed algorithm. © 2016 L. Qiaoyz, B. Zhangy, J. Suyz & X. Luyz.

关键词： Constrained optimization

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Qualitative action recognition by wireless radio signals in human-machine systems

arXiv

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arXiv 2017年

作者： Lv, Shaohe Lu, Yong Dong, Mianxiong Wang, Xiaodong Dou, Yong Zhuang, Weihua National Laboratory of Parallel and Distributed Processing National University of Defense Technology ChangshaHunan China Department of Information and Electronic Engineering Muroran Institute of Technology 27-1 Mizumoto-cho Muroran Hokkaido050-8585 Japan Department of Electrical and Computer Engineering University of Waterloo WaterlooON Canada

—Human-machine systems required a deep understanding of human behaviors. Most existing research on action recognition has focused on discriminating between different actions, however, the quality of executing an action has received little attention thus far. In this paper, we study the quality assessment of driving behaviors and present WiQ, a system to assess the quality of actions based on radio signals. This system includes three key components, a deep neural network based learning engine to extract the quality information from the changes of signal strength, a gradient based method to detect the signal boundary for an individual action, and an activity-based fusion policy to improve the recognition performance in a noisy environment. By using the quality information, WiQ can differentiate a triple body status with an accuracy of 97%, while for identification among 15 drivers, the average accuracy is 88%. Our results show that, via dedicated analysis of radio signals, a fine-grained action characterization can be achieved, which can facilitate a large variety of applications, such as smart driving assistants. Copyright © 2017, The Authors. All rights reserved.

关键词： Man machine systems

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ELM based Multiple Kernel k-means with Diversity-Induced Regularization

ELM based Multiple Kernel k-means with Diversity-Induced Reg...

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International Joint Conference on Neural Networks

作者： Yang Zhao Yong Dou Xinwang Liu Teng Li National University of Defense Technology National Laboratory for Parallel and Distributed Processing

ISBN: (纸本)9781509006212

Multiple-kernel k-means (MKKM) clustering has demonstrated good clustering performance by combining pre-specified kernels. In this paper, we argue that deep relationships within data and the complementary information among them can improve the performance of MKKM. To illustrate this idea, we propose a diversity-induced MKKM algorithm with extreme learning machine (ELM)-based feature extracting method. First, ELM, which has randomly chosen weights of hidden and output nodes, is applied to thoroughly extract features from data by generating different numbers of hidden nodes and using different functions. Second, an MKKM algorithm with diversity-induced regularization is utilized to explore the complementary information among kernels constructed from features. The problem could be solved efficiently by alternating optimization. Experimental results demonstrate that the proposed method outperforms state-of-the-art kernel methods.

关键词： Ecosystem models Elm Seeds Kernel output node Feature extraction Complementary Learning machines

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Dolphin:A Search Engine for OSS Based on Crowd Discussions across Communities

Dolphin:A Search Engine for OSS Based on Crowd Discussions a...

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2016 IEEE 7th International Conference on Software Engineering and Service science

作者： Yun Zhan Gang Yin Tao Wang Cheng Yang Zhixing Li Huaimin Wang National Laboratory for Parallel and Distributed Processing National University of Defense Technology

The global open source software resources have become an Internet-scale repository,which provide abundant resources for software ***,how to locate the desired resource efficiently and accurately from such large amounts is quite a challenge *** of recent works barely focus on the semantic similarity by analyzing the software itself,which often result in inappropriate *** this paper,we propose Dolphin,a global OSS search engine,which leverage the crowd discussions around OSS across communities for optimizing software *** employs the crowd wisdom as an important factor and combines it with the semantic similarity to optimize search results *** implement an OSS search engine based on the proposed approach,which provides online service *** experiments and user studies suggest the effectiveness of our approach.

关键词： Software retrieval ranking community influence

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A validation of SonarQube issues related to real bugs based on open source software

A validation of SonarQube issues related to real bugs based ...

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2016联合国际信息技术、机械与电子工程国际会议

作者： Xiang Hou Yao Lu Yiang Gan Mengwen Chen Tao Wang Gang Yin National Laboratory for Parallel and Distributed Processing National University of Defense Technology

ISBN: (纸本)9781510830028

High reliability is an essential factor of modern software. At the same time, as software complexity is increasing day by day, bug counts and rate inevitably rises, leading to undermine software reliability. To avoid this problem, programmers always use issue-finding tools(bug detection) to discover the defects from source code in development of software. Recently, software inspection has been shown to be an effective way to speed up the process of source code verification and to move a portion of discovered defects from test to coding phase. As we know, modern software is often developed over many years. During this time, the commit metadata is becoming an important source of social characteristics. In this paper, our aim is to devise an empirical method to assess the percentage and the types of the issues found by issue finding tools are actual defects of the software.

关键词： Bug Detection Source Control System SonarQube Java Git

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