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International Conference on Signal processing Proceedings (ICSP)

作者： Jinwei Xu Jingfei Jiang Yong Dou Xiaolong Shen Science and Technology on Parallel and distributed Processing Laboratory National University of Defense Technology Changsha China

Fingerprint matching is a key procedure in fingerprint identification applications. The fingerprint-matching algorithm based on minutiae is one of the most typical algorithms that can achieve a reasonably correct recognition rate. Performance and cost are two critical factors when implementing minutia-based matching algorithms in most embedded applications. A low-cost, fully pipelined architecture for minutia-based fingerprint matching is proposed in this paper. A regular matching unit with a pipeline of 13 stages is designed as the core of the architecture, interfacing with a two-port RAM and a DDR3 controller. We implemented the whole architecture on a Xilinx FPGA board with the Virtex VII XC7VX485T chip. The matching unit can run with a frequency of 330 MHz on the chip, which leads the system to achieve a throughput of about 430000 fingerprints per second when processing typical datasets. The unit only occupies 568 slices, which is less than 1% of the available chip resources. The board only consumes 16 W of power when run. The architecture can gain about twice the throughput of the 2.93 GHz Intel Xeon5670 CPU at a low logic cost and power.

关键词： Fingerprint recognition Throughput Random access memory Pipelines Computer architecture Field programmable gate arrays Algorithm design and analysis

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Accelerating Monte Carlo Simulation of Neutron Transport on the Intel MIC Architecture

Accelerating Monte Carlo Simulation of Neutron Transport on ...

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International Conference on Information science and Control Engineering (ICISCE)

作者： Xiantao Cui Jie Liu Lihua Chi Qinglin Wang Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China

ISBN: (纸本)9781467368513

Simulation of particle transport is critical for a great many of scientific and engineering domains. The Monte Carlo (MC) method is one of the most important numerical methods for the simulation of particle transport, and can simulate many complex types of particle transport. But the computation requirement of the MC simulation is very large. In 2010, Intel announced the Intel Many Integrated Core (MIC) architecture, which consists of many simple general-purpose cores and supports the well-known shared-memory execution model that is the base of most nodes in HPC machines. On account of the independence of simulation of each particle in the MC method, it is well-suited to accelerate the MC simulation on MIC. In this paper, an algorithm named MCNP-MIC based MIC is presented for MC simulation of neutron transport in the context of deep penetration problem, which includes the development of parallel random generator, the assignment of particle number based thread number and the design of high efficiency data structures for parallelism. Eventually, we get the results as follows: with the same problem scale and computational accuracy, the MCNPMIC algorithm has achieved roughly 5.6-fold speedup running on a 57-core MIC chip in comparison with the serial MCNP algorithm on an Intel Xeon E5-2670 CPU.

关键词： Microwave integrated circuits Neutrons Algorithm design and analysis Monte Carlo methods Data structures Graphics processing units

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Fisher Discriminant Analysis Random Forest for Online Class Incremental Learning

Fisher Discriminant Analysis Random Forest for Online Class ...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Wang Xiong Yijie Wang Li Cheng Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China

ISBN: (纸本)9781728111421;9781728111414

Class incremental learning needs to deal with a dynamic environment where data class appears incrementally, it is a challenge to learn new knowledge while preserving what has already been learned. On the other hand, due to the limited storage of the online scenario, algorithm is usually obstructed to frequently scan or simply store all historical data, it is another challenge to reduce the historical data storage for algorithm. Few existing work have addressed above challenges simultaneously. In this paper, we propose Fisher Discriminant Analysis Random Forest (FDARF), which consists of two parts, GHS (Generate Hierarchical Split) and RRS (Random Reform Subtree), that cooperatively operate. GHS combines FDA (Fisher Discriminant Analysis) with tree hierarchy to learn a hierarchical split of data space that provides strong ability for classification. The statistics in leaves (i.e. historical data) can be described by covariance matrix and further optimized by matrix sketching algorithm to reduce storage; for every tree initialized by GHS, RRS randomly reforms certain state subtree, which creates diversity that can be ensemble for ensuring effectiveness of class incremental learning. Extensive experiments on diverse datasets validate that FDARF can well adapt to the online class incremental learning.

关键词： Vegetation Training Covariance matrices Memory Forestry Learning systems

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Asia-Pacific Conference on Software Engineering

作者： Dongyang Hu Ming Chen Tao Wang Junsheng Chang Gang Yin Yue Yu Yang Zhang Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China

ISBN: (纸本)9781728119717;9781728119700

In the software development, it is not uncommon to find that several bug reports are related to many common code files, i.e., similar bugs. Similar bug recommendation is a meaningful task which can assist developers in bug triaging and fixing. As the state of the art, Yang et al.'s work presented an approach that combines TF-IDF method with word embedding model and achieved a good result. To further improve the performance of their approach, in this paper, we propose a novel approach using Document Embedding model. In our preliminary evaluation, we conduct the experiment on 13,090 bug reports from the Eclipse platform and the results show that our approach outperforms Yang et al.'s, with 7.89-8.96% of improvement.

关键词： Computer bugs Software engineering Task analysis Measurement Software Information retrieval Natural language processing

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Personalized reputation model in cooperative distributed systems

Personalized reputation model in cooperative distributed sys...

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16th IEEE International Conference on parallel and Distributed Systems, ICPADS 2010

作者： Liu, Wei Tang, Yang-Bin Wang, Huai-Min School of Computer National Univ. of Defense Technology Changsha Hunan China Institute of Science National Univ. of Defense Technology Changsha Hunan China National Laboratory for Parallel and Distributed Processing Changsha Hunan China

ISBN: (纸本)9780769543079

Reputation systems provide a promising way to build trust relationships between users in distributed cooperation systems, such as file sharing, streaming, distributed computing and social network, through which a user can distinguish good services or users from malicious ones and cooperate with them. However, most reputation models mainly focus on evaluating the quality of services in one dimension, but care less about the preferences of different users. This paper proposes a personalized reputation model which provides each user a personalized trust view on others according to his preferences. In our approach, we aggregate the users' preferences with collaborative filtering method and qualify it with user similarity which is integrated into the computing of reputation value. The experimental results suggest that our model can resist possible kinds of malicious behaviors efficiently. © 2010 IEEE.

关键词： Distributed computer systems

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Effective Anomaly Detection Based on Reinforcement Learning in Network Traffic Data

Effective Anomaly Detection Based on Reinforcement Learning ...

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International Conference on parallel and Distributed Systems (ICPADS)

作者： Zhongyang Wang Yijie Wang Hongzuo Xu Yongjun Wang Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology

Mixed-type data with both categorical and numerical features are ubiquitous in network security, but the existing methods are minimal to deal with them. Existing methods usually process mixed-type data through feature conversion, whereas their performance is downgraded by information loss and noise caused by the transformation. Meanwhile, existing methods usually superimpose domain knowledge and machine learning in which fixed thresholds are used. It cannot dynamically adjust the anomaly threshold to the actual scenario, resulting in inaccurate anomalies obtained, which results in poor performance. To address these issues, this paper proposes a novel Anomaly Detection method based on Reinforcement Learning, termed ADRL, which uses reinforcement learning to dynamically search for thresholds and accurately obtain anomaly candidate sets, fusing domain knowledge and machine learning fully and promoting each other. Specifically, ADRL uses prior domain knowledge to label known anomalies and uses entropy and deep autoencoder in the categorical and numerical feature spaces, respectively, to obtain anomaly scores combining with known anomaly information, which are integrated to get the overall anomaly scores via a dynamic integration strategy. To obtain accurate anomaly candidate sets, ADRL uses reinforcement learning to search for the best threshold. Detailedly, it initializes the anomaly threshold to get the initial anomaly candidate set and carries on the frequent rule mining to the anomaly candidate set to form the new knowledge. Then, ADRL uses the obtained knowledge to adjust the anomaly score and get the score modification rate. According to the modification rate, different threshold modification strategies are executed, and the best threshold, that is, the threshold under the maximum modification rate, is finally obtained, and the modified anomaly scores are obtained. The scores are used to re-carry out machine learning to improve the algorithm's accuracy for anomalo

关键词： Knowledge engineering Machine learning algorithms Conferences Distributed databases Reinforcement learning Telecommunication traffic Network security

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Entropy and Autoencoder-Based Outlier Detection in Mixed-Type Network Traffic Data

Entropy and Autoencoder-Based Outlier Detection in Mixed-Typ...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Zhongyang Wang Yijie Wang Zhenyu Huang Yongjun Wang Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology

Mixed-type data containing categorical and numerical features are pervasive in real life, but very limited outlier detection methods are available for these data. Some existing methods handle mixed-type data by feature converting, whereas their performance is downgraded by information loss and noise caused by the transformation. Meanwhile, the existing general algorithms cannot combine the characteristics of outliers in specific fields, leading to an unsatisfying performance in actual scenarios, such as the field of network security. This paper proposes a novel Entropy and Autoencoder-based Outlier Detection in mixed-type network traffic data, termed EAOD, which combines characteristics of outliers in specific fields and machine learning models to detect outliers. EAOD utilizes the expert rules made by domain knowledge summarized based on characteristics of existing outliers to label known outlier data. It performs holoentropy and deep autoencoder for the category and numerical feature spaces, respectively, in unlabeled data to obtain outlier scores integrated to get the final outlier scores via a dynamic integration strategy. Especially in the numerical feature space, to fully mine known outlier behavior patterns, deep autoencoders of outlier and normal types are constructed separately to capture unknown outliers jointly. Experiments show that EAOD significantly outperforms eight state-of-the-art outlier detectors on seven real network traffic datasets.

关键词： Telecommunication traffic Machine learning Detectors Network security Feature extraction Entropy Data models

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Optimizing Winograd-Based Fast Convolution Algorithm on Phytium Multi-Core CPUs

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Jisuanji Yanjiu yu Fazhan/Computer Research and Development 2020年第6期57卷 1140-1151页

作者： Wang, Qinglin Li, Dongsheng Mei, Songzhu Lai, Zhiquan Dou, Yong Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China College of Computer National University of Defense Technology Changsha410073 China

Convolutional neural networks (CNNs) have been extensively used in artificial intelligence fields such as computer vision and natural language processing. Winograd-based fast convolution algorithms can effectively reduce the computational complexity of convolution operations in CNNs so that they have attracted great attention. With the application of Phytium multi-core CPUs independently developed by the National University of Defense technology in artificial intelligence fields, there is strong demand of high-performance convolution primitives for Phytium multi-core CPUs. This paper proposes a new high-performance parallel Winograd-based fast convolution algorithm after studying architecture characteristics of Phytium multi-core CPUs and computing characteristics of Winograd-based fast convolution algorithms. The new parallel algorithm does not rely on general matrix multiplication routines, and consists of four stages: kernels transformation, input feature maps transformation, element-wise multiplication, and output feature maps inverse transformation. The data movements in all four stages have been collaboratively optimized to improve memory access performance of the algorithm. The custom data layouts, multi-level parallel data transformation algorithms and multi-level parallel matrix multiplication algorithm have also been proposed to support the optimization above efficiently. The algorithm is tested on two Phytium multi-core CPUs. Compared with Winograd-based fast convolution implementations in ARM Computer Library (ACL) and NNPACK, the algorithm can achieve speedup of 1.05~16.11 times and 1.66~16.90 times, respectively. The application of the algorithm in the open source framework Mxnet improves the forward-propagation performance of the VGG16 network by 3.01~6.79 times. © 2020, science Press. All right reserved.

关键词： Program processors

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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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Accelerating embarrassingly parallel algorithm on Intel MIC

Accelerating embarrassingly parallel algorithm on Intel MIC

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IEEE International Conference on Progress in Informatics and Computing (PIC)

作者： Qinglin Wang Jie Liu Xiantuo Tang Feng Wang Guitao Fu Zuocheng Xing Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China

ISBN: (纸本)9781479920327

The Embarrassingly parallel (EP) algorithm which is typical of many Monte Carlo applications provides an estimate of the upper achievable limits for double precision performance of parallel supercomputers. Recently, Intel released Many Integrated Core (MIC) architecture as a many-core co-processor. MIC often offers more than 50 cores each of which can run four hardware threads as well as 512-bit vector instructions. In this paper, we describe how the EP algorithm is accelerated effectively on the platforms containing MIC using the offload execution model. The result shows that the efficient implementation of EP algorithm on MIC can take full advantage of MIC's computational resources and achieves a speedup of 3.06 compared with that on Intel Xeon E5-2670 CPU. Based on the EP algorithm on MIC and an effective task distribution model, the implementation of EP algorithm on a CPU-MIC heterogeneous platform achieves the performance of up to 2134.86 Mop/s and 4.04 times speedup compared with that on Intel Xeon E5-2670 CPU.

关键词： Microwave integrated circuits Computer architecture Vectors Algorithm design and analysis Graphics processing units Clustering algorithms Load modeling

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