检索结果-内蒙古大学图书馆

From WiFi to WiMAX: Efficient GPU-based Parameterized Transceiver across Different OFDM Protocols

KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2013年第8期7卷 1911-1932页

作者： Li, Rongchun Dou, Yong Zhou, Jie Li, Baofeng Xu, Jinbo Natl Univ Def Technol Natl Lab Parallel & Distributed Proc Changsha 410073 Hunan Peoples R China

Orthogonal frequency-division multiplexing (OFDM) has become a popular modulation scheme for wireless protocols because of its spectral efficiency and robustness against multipath interference. Although the components of various OFDM protocols are functionally similar, they remain distinct because of the characteristics of the environment. Recently, graphics processing units (GPUs) have been used to accelerate the signal processing of the physical layer (PHY) because of their great computational power, high development efficiency, and flexibility. In this paper, we describe the implementation of parameterized baseband modules using GPUs for two different OFDM protocols, namely, 802.11a and 802.16. First, we introduce various modules in the modulator/demodulator parts of the transmitter and receiver and analyze the computational complexity of each module. We then describe the integration of the GPU-based baseband modules of the two protocols using the parameterized method. GPU-based implementations are addressed to explain how to accelerate the baseband processing to archive real-time throughput. Finally, the performance results of each signal processing module are evaluated and analyzed. The experiments show that the GPU-based 802.11a and 802.16 PHY meet the real-time requirement and demonstrate good bit error ratio (BER) performance. The performance comparison indicates that our GPU-based implemented modules have better flexibility and throughput to the current ones.

关键词： GPU SDR OFDM WiFi WiMAX

来源：评论

学校读者我要写书评

暂无评论

Applying partial power-gating to bit-sliced network-on-chip

引用

MICROELECTRONICS JOURNAL 2015年第11期46卷 1002-1011页

作者： Wang, Feng Tang, Xiantuo Xing, Zuocheng Natl Univ Def Technol Natl Lab Parallel & Distributed Proc Changsha 410073 Hunan Peoples R China

In the many-core systems, network-on-chip (NoC) serves as an efficient and scalable architecture to connect numerous on-chip resources, whereas it encounters the crisis of the increasing leakage power as technology is continually scaling down. Power-gating which is a representative low-power technique can be utilized to mitigate the increasing leakage power, but the disconnection problem suffered in the conventional power-gated NoC may severely affect network performance. In this paper, we propose a novel partial power-gating approach to avoid the performance loss caused by the disconnection. Firstly, we utilize the asymmetrical bit-slicing scheme to split router into two slices. After the bit-slicing of router datapath, the wide slices can be switched off to save some leakage power by using partial powergating, but all narrow slices should be kept in ever-active state to avoid the disconnection. Next, owing to the slicing of router datapath, we redefine the packet format for the packet's slicing and transferring, and present two essential conversion modules to achieve packet's slicing and reassembling. In the synthetic traffic simulation, our design gains considerable power-saving at low-load and exhibits better performance behavior than the conventional power-gated design. The application simulation shows that our design can averagely save 27.5% of total power compared with the baseline design, and reduce 45.0% packet latency on average when compared with the conventional power-gated design. On balance, the bit-sliced NoC with partial power-gating has a better tradeoff between performance and powerefficiency. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Network-on-chip Bit-slicing Power-gating Low-power

来源：评论

学校读者我要写书评

暂无评论

Beyond top-N accuracy indicator: a comprehensive evaluation indicator of CNN models in image classification

引用

IET COMPUTER VISION 2020年第6期14卷 407-414页

作者： Liu, Yuntao Dou, Yong Qiao, Peng Natl Univ Def Technol Natl Lab Parallel & Distributed Proc Changsha 410073 Hunan Peoples R China

Nowadays, a large number of deep convolutional neural network (CNN) models are applied to image classification tasks. However, the authors find that the most widely used evaluation indicator, the Top-N Accuracy indicator, cannot discriminate these models effectively. In this study, they propose a new indicator called Maximum-Spanning-Confusion-Tree indicator to solve this problem. The Maximum-Spanning-Confusion-Tree indicator is computed based on the hierarchical structure of the Maximum Spanning Confusion Tree of the deep CNN model on the dataset and reflect the ability of deep CNN models to discriminate confused categories in the dataset. The hierarchical structure of the Maximum Spanning Confusion Tree can reveal the confused category set of one selected category in the dataset efficiently and flexibly. Experiments show that they can discriminate ten different deep CNN models more accurately with the Maximum Spanning Confusion Tree indicator than the Top-N Accuracy indicator and the Maximum Spanning Confusion Tree intuitively shows the distribution of confused category sets in the dataset so they can find out the weakness of deep CNN models effectively.

关键词： deep convolutional neural network models Maximum-Spanning-Confusion-Tree indicator comprehensive evaluation indicator deep CNN model neural nets different deep CNN models trees (mathematics) Maximum Spanning Confusion Tree indicator Top-N Accuracy indicator confused category set top-N accuracy indicator widely used evaluation indicator image classification

来源：评论

学校读者我要写书评

暂无评论

GBCNN: A Full GPU-Based Batch Multi-Task Cascaded Convolutional Networks

引用

IEEE ACCESS 2019年 7卷 20225-20234页

作者： Li, Shijie Dou, Yong Xu, Jinwei Yang, Ke Li, Rongchun Natl Univ Def Technol Natl Lab Parallel & Distributed Proc Changsha 410073 Hunan Peoples R China

Recently, the face detection and alignment is so popular and widely used in many research and application fields. Many superior face detection algorithms such as multi-task cascade convolutional network have been presented. However, it has difficulty in predicting faces among the big scale images in real time due to its three stages cascade architecture with less optimization. In this paper, we propose a full GPU-based batch multi-task cascade convolutional network which is carefully designed and optimized in each step to gain a superior speed performance. In addition, we present a novel parallel memory allocation strategy, which further enables our algorithm to support the batch operation, so that the system throughput increases significantly. In the experiment, our method achieves up to 300fps, over 600% speedup with an equal accuracy over the state-of-the-art methods on the face detection benchmarks.

关键词： CNN GPU face detection

来源：评论

学校读者我要写书评

暂无评论

Research and performance evaluation of data replication technology in distributed storage systems

引用

COMPUTERS & MATHEMATICS WITH APPLICATIONS 2006年第11期51卷 1625-1632页

作者： Wang, Yijie Li, Sijun Natl Univ Def Technol Inst Comp Natl Lab Parallel & Distributed Proc Changsha 410073 Peoples R China

Replication is a key technology of distributed storage systems. In this paper, an indirect replication algorithm is proposed following the intrinsic characteristic of distributed storage systems and the peer-to-peer model. In the indirect replication algorithm, the data object is partitioned into several data blocks. These data blocks are encoded in order that there is data redundancy between data blocks. Comparing with the traditional replication algorithm, the indirect replication algorithm has less granularity of replication, less bandwidth and storage costs, and provides higher availability, durability, and security. The performance evaluation shows that the encoding and decoding times are proportional to the data size, and that the irregular cascade bipartite graphs are of great advantage in improving the success ratio of data recovery. Finally, if the number of data blocks used to recover the data object is larger than a certain value, the success ratio of data recovery approaches 100%. (c) 2006 Elsevier Ltd. All rights reserved.

关键词： distributed storage system replication data availability code performance evaluation

来源：评论

学校读者我要写书评

暂无评论

An automatic learning rate decay strategy for stochastic gradient descent optimization methods in neural networks

引用

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 2022年第10期37卷 7334-7355页

作者： Wang, Kang Dou, Yong Sun, Tao Qiao, Peng Wen, Dong Natl Univ Def Technol Sch Comp Natl Lab Parallel & Distributed Proc Changsha 410073 Peoples R China

Stochastic Gradient Descent (SGD) series optimization methods play the vital role in training neural networks, attracting growing attention in science and engineering fields of the intelligent system. The choice of learning rates affects the convergence rate of SGD series optimization methods. Currently, learning rate adjustment strategies mainly face the following problems: (1) The traditional learning rate decay method mainly adopts manual manner during training iterations, the small learning rate produced from which causes slow convergence in training neural networks. (2) Adaptive method (e.g., Adam) has poor generalization performance. To alleviate the above issues, we propose a novel automatic learning rate decay strategy for SGD optimization methods in neural networks. On the basis of the observation that the convergence rate's upper bound enjoys minimization in a specific iteration concerning the current learning rate, we first present the expression of the current learning rate determined by historical learning rates. And merely one extra parameter is initialized to generate automatic decreasing learning rates during the training process. Our proposed approach is applied to SGD and Momentum SGD optimization algorithms, and concrete theoretical proof explains its convergence. Numerical simulations are conducted on the MNIST and Cifar-10 data sets with different neural networks. Experimental results show that our algorithm outperforms existing classical ones, achieving faster convergence rate, better stability, and generalization performance in neural network training. It also lays a foundation for large-scale parallel search of initial parameters in intelligent systems.

关键词： automatic learning rate decay strategy fast convergence generalization performance neural networks stochastic gradient descent optimization algorithms training stability

来源：评论

学校读者我要写书评

暂无评论

Exploring frame segmentation networks for temporal action localization

引用

JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2019年第May期61卷 296-302页

作者： Yang, Ke Shen, Xiaolong Qiao, Peng Li, Shijie Li, Dongsheng Dou, Yong Natl Univ Def Technol Coll Comp Natl Lab Parallel & Distributed Proc Changsha Hunan Peoples R China

Temporal action localization is an important task of computer vision. Though many methods have been proposed, it still remains an open question how to predict the temporal location of action segments precisely. Most state-of-the-art works train action classifiers on video segments pre-determined by action proposal. However, recent work found that a desirable model should move beyond segment-level and make dense predictions at a fine granularity in time to determine precise temporal boundaries. In this paper, we propose a Frame Segmentation Network (FSN) that places a temporal CNN on top of the 2D spatial CNNs. Spatial CNNs are responsible for abstracting semantics in spatial dimension while temporal CNN is responsible for introducing temporal context information and performing dense predictions. The proposed FSN can make dense predictions at frame-level for a video clip using both spatial and temporal context information. FSN is trained in an end-to-end manner, so the model can be optimized in spatial and temporal domain jointly. We also adapt FSN to use it in weakly supervised scenario (WFSN), where only video level labels are provided when training. Experiment results on public dataset show that FSN achieves superior performance in both frame-level action localization and temporal action localization. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Action detection Temporal action localization Convolutional Neural Network

来源：评论

学校读者我要写书评

暂无评论

ESDU: An elastic stripe-based delta update method for erasure-coded cross-data center storage systems

引用

JOURNAL OF parallel AND distributed COMPUTING 2022年第0期167卷 173-186页

作者： Bao, Han Wang, Yijie Natl Univ Def Technol Sch Comp Natl Lab Parallel & Distributed Proc Changsha 410073 Peoples R China

The erasure-coded cross-data center storage system can achieve high disaster tolerance and low redundancy. But as it has large cross-data center update traffic, its data update time is long. In erasure-coded storage systems, each data object is sequentially divided into several stripes, and each stripe consists of several data packets. When erasure-coded stripes do not undergo insertion or deletion, existing work can effectively reduce cross-data center update traffic by performing delta updates- delta update methods can update the old stripe without transferring matched packets that are new and old stripes' duplicate packets with the same offset-within-stripe. However, because existing delta update methods' stripe size is fixed, when a stripe undergoes insertion or deletion, its subsequent stripes' duplicate packets' offset-within-stripe will change. In this scenery, the matched packet number is small, resulting in large cross-data center update traffic. This paper proposes an elastic stripe-based delta update method for erasure-coded cross-data center storage systems (ESDU). Under insertion or deletion, ESDS tries to avoid duplicate packets' offset-within-stripe changing (i.e., maximizing matched packets) by adjusting the stripes' size flexibly according to the duplicate packet locating result. So, it can reduce cross-data center traffic. Moreover, ESDU can optimize stripes' update topology based on the location information of storage nodes to reduce cross-data center update traffic further. In addition, we implement an erasure-coded cross-data center storage system adopting ESDU, called ECESD. Experiments with the workloads derived from EduCoder's real-world trace show that compared with the existing erasure-coded cross-data center storage system adopting the fixed stripe-based delta update method, ECESD reduces average update time by 89.6%. Moreover, compared with a replication-based storage system with a delta update method (HadoopRsync), ECESD achieves an 8.3%

关键词： Erasure code Disaster tolerance distributed storage system Delta update method Cross-data center storage

来源：评论

学校读者我要写书评

暂无评论

A Novel Architecture to Eliminate Bottlenecks in a parallel Tiled QRD Algorithm for Future MIMO Systems

引用

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2017年第1期64卷 26-30页

作者： Liu, Cang Xing, Zuocheng Yuan, Luechao Tang, Chuan Zhang, Yang Natl Univ Def Technol Natl Lab Parallel & Distributed Proc Changsha 410073 Hunan Peoples R China

QR decomposition (QRD) is one of the performance bottlenecks in a lot of high performance wireless communication algorithms and should have the flexibility property for future multiple-input multiple-output systems. However, the existing QRD architectures only focus on several fixed dimension matrices. The parallel tiled QRD algorithm is a perfect choice to implement QRD for its flexibility and modularity property. The size of the tile is set to 2 x 2 instead of the traditional 200 x 200 or more to support flexible antenna configurations in this brief. Using a look-ahead technique and the property of unitary matrix, a novel algorithm based on a modified Gram-Schmidt (MGS) algorithm is proposed for the bottleneck operations (GEQRT and TTQRT) of the parallel tiled QRD algorithm. A corresponding hardware architecture is also designed with the proposed algorithm. The implementation results show that the hardware architecture based on the proposed algorithm achieves a 2.7x reduction in normalized processing latency, compared with the one based on the traditional MGS algorithm.

关键词： Modified Gram-Schmidt (MGS) parallel tiled QR decomposition (QRD) wireless communication

来源：评论

学校读者我要写书评

暂无评论

High Precision Low Complexity Matrix Inversion Based on Newton Iteration for Data Detection in the Massive MIMO

引用

IEEE COMMUNICATIONS LETTERS 2016年第3期20卷 490-493页

作者： Tang, Chuan Liu, Cang Yuan, Luechao Xing, Zuocheng Natl Univ Def Technol Natl Lab Parallel & Distributed Proc Changsha 410073 Hunan Peoples R China

Currently, massive multiple-input multiple-output (MIMO) is one of the most promising wireless transmission technologies for 5G. Massive MIMO requires handling with large-scale matrix computation, especially for matrix inversion. In this letter, we find that matrix inversion based on Newton iteration (NI) is suitable for data detection in massive MIMO system. In contrast with recently proposed polynomial expansion (PE) method for matrix inversion, we analyze both the algorithm complexity and precision in detail, and propose a diagonal band Newton iteration (DBNI) method, which is an approximate method for NI. Compared with PE method, DBNI can obtain higher precision and approximately equal complexity, and we give an explanation of how to select the bandwidth of DBNI.

关键词： Massive MIMO data detection matrix inversion Newton iteration Neumann series

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：