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IEEE International Conference on Communications

作者： Yu Gu Yusheng Ji Jie Li Biao Han Baohua Zhao Information Systems Architecture Science Research Division National Institute of Informatics Department of Computer Science University of Tsukuba Tsukuba Science City State Key Laboratory of Networking and Switching Technology

ISBN: (纸本)9781457720529

This paper exploits sink mobility to prolong the network lifetime in wireless sensor networks (WSNs) where the information delay caused by moving the sink should be bounded. We build a unified framework for analyzing this joint sink mobility and routing problem. We offer a mathematical modeling that is general and captures diversified issues, e.g. sink mobility, routing, delay, etc. We discuss the induced subproblems and present efficient solutions for them. Then, we generalize these solutions and propose a polynomial-time optimal algorithm for the origin problem. In simulations, we show the benefits of involving a mobile sink. We also show that the impact of the delay bound on the network lifetime.

关键词： Sinks Wireless sensor networks Routing Problem mobility mathematical modeling

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Exploring spatial-temporal multi-frequency analysis for high-fidelity and temporal-consistency video prediction

arXiv

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arXiv 2020年

作者： Jin, Beibei Hu, Yu Tang, Qiankun Niu, Jingyu Shi, Zhiping Han, Yinhe Li, Xiaowei Research Center for Intelligent Computing Systems State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Capital Normal University

Video prediction is a pixel-wise dense prediction task to infer future frames based on past frames. Missing appearance details and motion blur are still two major problems for current predictive models, which lead to image distortion and temporal inconsistency. In this paper, we point out the necessity of exploring multi-frequency analysis to deal with the two problems. Inspired by the frequency band decomposition characteristic of Human Vision System (HVS), we propose a video prediction network based on multi-level wavelet analysis to deal with spatial and temporal information in a unified manner. Specifically, multi-level spatial discrete wavelet transform decomposes each video frame into anisotropic sub-bands with multiple frequencies, helping to enrich structural information and reserve fine details. On the other hand, multi-level temporal discrete wavelet transform which operates on time axis decomposes the frame sequence into sub-band groups of different frequencies to accurately capture multi-frequency motions under fixed frame rate. Extensive experiments on diverse datasets demonstrate that our model shows significant improvements on fidelity and temporal consistency over the state-of-the-art works. Copyright © 2020, The Authors. All rights reserved.

关键词： Discrete wavelet transforms

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Exploring Spatial-Temporal Multi-Frequency Analysis for High-Fidelity and Temporal-Consistency Video Prediction

Exploring Spatial-Temporal Multi-Frequency Analysis for High...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Beibei Jin Yu Hu Qiankun Tang Jingyu Niu Zhiping Shi Yinhe Han Xiaowei Li Research Center for Intelligent Computing Systems State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Capital Normal University

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Video prediction is a pixel-wise dense prediction task to infer future frames based on past frames. Missing appearance details and motion blur are still two major problems for current models, leading to image distortion and temporal inconsistency. We point out the necessity of exploring multi-frequency analysis to deal with the two problems. Inspired by the frequency band decomposition characteristic of Human Vision System (HVS), we propose a video prediction network based on multi-level wavelet analysis to uniformly deal with spatial and temporal information. Specifically, multi-level spatial discrete wavelet transform decomposes each video frame into anisotropic sub-bands with multiple frequencies, helping to enrich structural information and reserve fine details. On the other hand, multilevel temporal discrete wavelet transform which operates on time axis decomposes the frame sequence into sub-band groups of different frequencies to accurately capture multifrequency motions under a fixed frame rate. Extensive experiments on diverse datasets demonstrate that our model shows significant improvements on fidelity and temporal consistency over the state-of-the-art works. Source code and videos are available at https://***/Bei-Jin/STMFANet.

关键词： Discrete wavelet transforms Predictive models Wavelet analysis Streaming media Time-frequency analysis

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Analysis and Design of Mobile Model for DTN

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Chinese Journal of Electronics 2023年第4期18卷 635-638页

作者： Zhiting Lin Yugui Qu Yusheng Ji Baohua Zhao Department of Computer Science University of Science and Technology of China Hefei China Province Key Laboratory of Software in Computing and Communication Hefei China Information Systems Architecture Science Research Division National Institute of Informatics Tokyo Japan

Delay tolerant networks may become unexpectedly partitioned due to node mobility or variation in signal strength. However, most widely used models in some relative works are generally very simplistic. In order to exploit intelligent forwarding algorithms, a novel nodal mobile model of delay tolerant networks is presented to map reality with more accuracy. And several approaches are introduced to analyze the network structure, such as n-cliques, n-clans, degree, closeness and betweenness. Our research results showed that the centralizations became smaller when the wireless connections were concerned. It meant that quite a number of nodes became potential relays because of the new structure of networks.

关键词： Wireless communication Analytical models Wireless sensor networks Protocols Social networking (online) Sensitivity analysis Social network theory

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Parallelization of LU decomposition on the godson-Tv1 many-core architecture

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Jisuanji Xuebao/Chinese Journal of computers 2009年第11期32卷 2157-2167页

作者： Long, Guo-Ping Fan, Dong-Rui Key Laboratory of Computer Systems and Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China

The many-core architecture is increasingly becoming a promising computing platform due to the advancement of semi-conductor technology. LU decomposition is a widely used kernel in both scientific and engineering computations. Although there are a lot of related works on traditional parallel architectures, there is still little work focusing on parallelizing it on many-core architectures. This paper investigates this problem from three aspects: load balancing, latency hiding and performance modeling. There are three contributions of this work: Firstly, a novel load balancing technique has been introduced to overcome the limitations of 2D scatter decomposition. Experimental results show that the proposed scheme achieves 20% performance improvement without optimization and 40% improvement after optimization. Secondly, an analytical performance model is presented. Quantitative experimental study shows that by carefully hiding memory latency through on chip memory hierarchy and for a selected block size, the upper bound of theoretical performance can be approximated by experiments. Experimental results also reveal two primary causes which make theoretical speedup hard to achieve: limited DRAM bandwidth and resource contention of on-chip network.

关键词： Parallel architectures

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A service-oriented priority-based resource scheduling scheme for virtualized utility computing 08

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Proceedings of the 15th international conference on High performance computing

作者： Ying Song Yaqiong Li Hui Wang Yufang Zhang Binquan Feng Hongyong Zang Yuzhong Sun Key Lab. of Computer System and Architecture Inst. of Computing Techn. Chinese Academy of Sciences China and National Research Center for Intelligent Computing Systems ICT China and Graduate Univ. of Chinese Academy of Sciences China Key Laboratory of Computer System and Architecture Institute of Computing Technology Chinese Academy of Sciences China and National Research Center for Intelligent Computing Systems ICT China

ISBN: (纸本)9783540898931

In order to provide high resource utilization and QoS assurance inutility computing hosting concurrently various services, this paper proposes aservice computing framework-RAINBOW for VM(Virtual Machine)-basedutility computing. In RAINBOW, we present a priority-based resourcescheduling scheme including resource flowing algorithms (RFaVM) to optimizeresource allocations amongst services. The principle of RFaVM is preferentiallyensuring performance of some critical services by degrading of others to someextent when resource competition arises. Based on our prototype, we evaluateRAINBOW and RFaVM. The experimental results show that RAINBOWwithout RFaVM provides 28%-324% improvements in service performance,and 26% higher the average CPU utilization than traditional service computingframework (TSF) in typical enterprise environment. RAINBOW with RFaVMfurther improves performance by 25%-42% for those critical services whileonly introducing up to 7% performance degradation to others, with 2%-8%more improvements in resource utilization than RAINBOW without RFaVM.

关键词： resource scheduling utility computing virtualization

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ACG-Engine: An Inference Accelerator for Content Generative Neural Networks

ACG-Engine: An Inference Accelerator for Content Generative ...

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IEEE International Conference on computer-Aided Design

作者： Haobo Xu Ying Wang Yujie Wang Jiajun Li Bosheng Liu Yinhe Han University of Chinese Academy of Sciences State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Research Center for Intelligent Computing Systems Institute of Computing Technology Chinese Academy of Sciences

The technological breakthrough in Generative Adversarial Networks (GAN) has propelled the advancement of content generative applications such as AI-based paintings, style transfer, and music composition. However, in contrast to previous deep learning models for prediction and categorization, generative networks generally rely on instance normalization (IN) layer for better feature distribution, which performs significantly better than batch normalization(BN) in image style-transfer, image to image translation, etc. Unlike batch or group normalization that can be fused into convolutional layers and ignored during the network inference stage, an instance normalization layer induces intensive computation and memory access. However, prior deep learning accelerator designs for traditional Neural Network and Generative Adversarial Networks mostly focus on the acceleration of convolution and deconvolution layer but lack of support for IN operations, which could become a performance bottleneck on edge devices with insufficient computational power. To address this problem, we propose an inference accelerator for content generation (ACG-Engine) aimed to support the fundamental operations of generative networks, including convolution layers, deconvolution layers, specifically instance normalization layer. We performed a hardware-aware mathematical transformation of the IN operation for less computation complexity and memory-friendliness, so that it can be efficiently mapped to the classic 2D processing element array. Owing to the proposed optimization techniques, ACG-Engine achieves 4.56X speedup and improve power efficiency up to 29X compared to prior baseline acceleration scheme in generative network acceleration. In addition, ACG-Engine can achieve performance comparable to the classic CNN-specific accelerators with negligible power consumption and area overhead.

关键词： Performance evaluation Deep learning Deconvolution Convolution Neural networks Generative adversarial networks Acceleration

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Lightdet: A Lightweight and Accurate Object Detection Network

Lightdet: A Lightweight and Accurate Object Detection Networ...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Qiankun Tang Jie Li Zhiping Shi Yu Hu Research Center for Intelligent Computing Systems State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing Advanced Innovation Center for Imaging Theory and Technology Capital Normal University

ISBN: (数字)9781509066315

ISBN: (纸本)9781509066322

The extensive computational burden limits the usage of accurate but complex object detectors in resource-bounded scenarios. In this paper, we present a lightweight object detector, named LightDet, to address this dilemma. We design a lightweight backbone that is able to capture rich low-level features by the proposed Detail-Preserving Module. To effectively aggregate bottom and top-down features, we introduce an efficient Feature-Preserving and Refinement Module. A lightweight prediction head is employed to further reduce the entire network complexity. Experimental results show that our LightDet achieves 75.5% mAP on PASCAL VOC 2007 at the speed of 250 FPS and 24.0% mAP on MS COCO dataset.

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KFS-LIO: key-Feature Selection for Lightweight Lidar Inertial Odometry

KFS-LIO: Key-Feature Selection for Lightweight Lidar Inertia...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Wei Li Yu Hu Yinhe Han Xiaowei Li Research Center for Intelligent Computing Systems Institute of Computing Technology the Chinese Academy of Sciences Beijing China State Key Laboratory of Computer Architecture Institute of Computing Technology the Chinese Academy of Sciences Beijing China

Feature-based lidar odometry methods have attracted increasing attention due to their low computational cost. However, theoretically analysis of the effect of extracted features on pose estimation is still lacked. In this paper, we propose a method of key-feature selection for lightweight lidar inertial odometry, KFS-LIO, to further enhance the real-time performance by selecting the most effective subset of lidar feature constraints. Aiming at explaining the correlation between the feature distribution and state errors, a quantitative evaluation method of lidar constraints is introduced. In addition, to avoid recalculating the reprojection matrices in de-skewing step, we use the intermediate variables in IMU preintegration to compensate for lidar motion distortion. The experimental results demonstrate that KFS-LIO can reduce half of the LOAM features and provide comparable accuracy with the state-of-the-art odometry.

关键词： Laser radar Correlation Automation Conferences Pose estimation Feature extraction Distortion

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Application of fuzzy neural network in the system of concrete undamaged inspection

Application of fuzzy neural network in the system of concret...

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World Congress on Intelligent Control and Automation (WCICA)

作者： Jing Xu Qingchun Meng Songsen Yang Wen Zhang Changhong Song Department of Civil Engineering Qingdao Institute of Architecture and Engineering Qingdao China Department of Computer Science Ocean University of China Qingdao China State Key Laboratory of Intelligent Technology and Systems Tsinghua University Beijing China

ISBN: (纸本)0780382730

The accuracy of concrete strength inspection has a great influence on the safety evaluation of the building. In order to increase the accuracy, Fuzzy Neural Network (FNN) was built up to evaluate concrete stmngth: It takes full advantage of the characteristics of the common concrete testing methods: drill and rebound, and the abilities of FNN including automatic learning, generation and fuzzy logic inference. The experiment shows that the max relative error of the predicted results is 1.12%, which is satisfied with the requirements of the engineering. The method effieieatly maps the complex non-linear relationship between the drill values and the rebound values, and provides a efficient way for the concrete strength inspection and evaluation.

关键词： Fuzzy neural networks Intelligent networks Concrete Inspection Application software computer science Oceans Civil engineering Intelligent systems Intelligent structures

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