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A Case for Adaptive Resource Management in Alibaba Datacenter Using Neural Networks

Journal of computer science & technology 2020年第1期35卷 209-220页

作者： Sa Wang Yan-Hai Zhu Shan-Pei Chen Tian-Ze Wu Wen-Jie Li Xu-Sheng Zhan Hai-Yang Ding Wei-Song Shi Yun-Gang Bao State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of Sciences Beijing 100190China University of Chinese Academy of Sciences Beijing 100049China Peng Cheng Laboratory Shenzhen 518055China Alibaba Inc. Hangzhou 311121China Department of Computer Science Wayne State UniversityMichiganMI 48202U.S.A

Both resource efficiency and application QoS have been big concerns of datacenter operators for a long time,but remain to be *** resource utilization increases the risk of resource contention between co-located workload,which makes latency-critical(LC)applications suffer unpredictable,and even unacceptable *** of prior work devotes the effort on exploiting effective mechanisms to protect the QoS of LC applications while improving resource *** this paper,we propose MAGI,a resource management runtime that leverages neural networks to monitor and further pinpoint the root cause of performance interference,and adjusts resource shares of corresponding applications to ensure the QoS of LC *** is a practice in Alibaba datacenter to provide on-demand resource adjustment for applications using neural *** experimental results show that MAGI could reduce up to 87.3%performance degradation of LC application when co-located with other antagonist applications.

关键词： resource management neural network resource efficiency tail latency

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A survey of neural network accelerator with software development environments

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Journal of Semiconductors 2020年第2期41卷 20-28页

作者： Jin Song Xuemeng Wang Zhipeng Zhao Wei Li Tian Zhi SKL of Computer Architecture Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China Cambricon Tech.Ltd Beijing 100191China University of Science and Technology of China Hefei 230026China

Recent years,the deep learning algorithm has been widely deployed from cloud servers to terminal *** researchers proposed various neural network accelerators and software development *** this article,we have reviewed the representative neural network *** an entirety,the corresponding software stack must consider the hardware architecture of the specific accelerator to enhance the end-to-end *** we summarize the programming environments of neural network accelerators and optimizations in software ***,we comment the future trend of neural network accelerator and programming environments.

关键词： neural network accelerator compiling optimization programming environments

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Meta-Learning Based Infrared Ship Object Detection Model for Generalization to Unknown Domains

SSRN

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SSRN 2023年

作者： Feng, Hui Tang, Wei Xu, Haixiang Jiang, Chengxin Ge, Shuzhi Sam He, Jianhua Key Laboratory of High-Performance Ship Technology of Ministry of Education Wuhan University of Technology Wuhan430063 China School of Naval Architecture Ocean and Energy Power Engineering Wuhan University of Technology Wuhan430063 China Department of Electrical and Computer Engineering National University of Singapore Singapore117576 Singapore School of Computer Science and Electronic Engineering University of Essex CO4 3SQ United Kingdom

Infrared images exhibit considerable variations in probability distributions, stemming from the utilization of distinct infrared sensors and the influence of diverse environmental conditions. The variations pose great challenges for deep learning models to detect ship objects and adapt to unseen maritime environments. To address the domain shift problem, we propose an end-to-end infrared ship object detection model based on meta-learning neural network to improve domain adaptation for target domain where data is not available at training phase. Different from existing domain generalization methods, the novelty of our model lies in the effective exploition of meta-learning and domain adaptation, ensuring that the extracted domain-independent features are meaningful and domain-invariant at the semantic level. Firstly, a double gradient-based meta-learning algorithm is designed to solve the common optimal descent direction between different domains through two gradient updates in the inner and outer loops. The algorithm enables extraction of domain-invariant features from the pseudo-source and pseudo-target domain data. Secondly, a domain discriminator with dynamic-weighted gradient reversal layer (DWGRL) is designed to accurately classify domain-invariant features and provide additional global supervision information. Finally, a multi-scale feature aggregation method is proposed to improve the extraction of multi-scale domain-invariant features. It can effectively fuse local features at different scales and global features of targets. Extensive experimental results conducted in real nighttime water surface scenes demonstrate that the proposed model achieves very high detection accuracy on target domain data, even no target domain data was used during the training phase. Compared to the existing methods, our method not only improves the detection accuracy of infrared ships by 18%, but also exhibits the smallest standard deviation with a value of 0.93, indicating its su

关键词： Object detection

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Bayesian uncertainty analysis for underwater 3D reconstruction with neural radiance fields

arXiv

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arXiv 2024年

作者： Lian, Haojie Li, Xinhao Qu, Yilin Du, Jing Meng, Zhuxuan Liu, Jie Chen, Leilei Key Laboratory of In-situ Property-improving Mining of Ministry of Education Taiyuan University of Technology Taiyuan100190 China Henan International Joint Laboratory of Structural Mechanics and Computational Simulation College of Architecture and Civil Engineering Huanghuai University Zhumadian463000 China School of Marine Science and Technology Northwestern Polytechnical University Xi’an710072 China Unmanned Vehicle Innovation Center Ningbo Institute of Northwestern Polytechnical University Ningbo315103 China Key Laboratory of Unmanned Underwater Vehicle Technology of Ministry of Industry and Information Technology Northwestern Polytechnical University Xi’an710072 China Center for Strategic Assessment and Consulting Academy of Military Science Beijing100091 China Computer Engineering Department Taiyuan Institute of Technology Taiyuan030008 China

Neural radiance fields (NeRFs) are a deep learning technique that can generate novel views of 3D scenes using sparse 2D images from different viewing directions and camera poses. As an extension of conventional NeRFs in underwater environment, where light can get absorbed and scattered by water, SeaThru-NeRF was proposed to separate the clean appearance and geometric structure of underwater scene from the effects of the scattering medium. Since the quality of the appearance and structure of underwater scenes is crucial for downstream tasks such as underwater infrastructure inspection, the reliability of the 3D reconstruction model should be considered and evaluated. Nonetheless, owing to the lack of ability to quantify uncertainty in 3D reconstruction of underwater scenes under natural ambient illumination, the practical deployment of NeRFs in unmanned autonomous underwater navigation is limited. To address this issue, we introduce a spatial perturbation field Dω based on Bayes’ rays in SeaThru-NeRF and perform Laplace approximation to obtain a Gaussian distribution N(0, Σ) of the parameters ω, where the diagonal elements of Σ correspond to the uncertainty at each spatial location. We also employ a simple thresholding method to remove artifacts from the rendered results of underwater scenes. Numerical experiments are provided to demonstrate the effectiveness of this approach. Copyright © 2024, The Authors. All rights reserved.

关键词： Uncertainty analysis

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Edge Computing-Based Joint Client Selection and Networking Scheme for Federated Learning in Vehicular IoT

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china Communications 2021年第6期18卷 39-52页

作者： Wugedele Bao Celimuge Wu Siri Guleng Jiefang Zhang Kok-Lim Alvin Yau Yusheng Ji School of computer science and information engineering Hohhot Minzu CollegeHohhot 010051China Graduate School of Informatics and Engineering The University of Electro-Communications1-5-1ChofugaokaChofu-shiTokyo182-8585 Japan Institute of Intelligent Media Technology Communication University of ZhejiangZhejiang 310018China School of Science and Technology Sunway University5Jalan UniversitiBandar Sunway47500 Petaling JayaSelangorMalaysia Information Systems Architecture Research Division National Institute of Informatics2-1-2HitotsubashiChiyoda-kuTokyo 101-8430 Japan

In order to support advanced vehicular Internet-of-Things(IoT)applications,information exchanges among different vehicles are required to find efficient solutions for catering to different application requirements in complex and dynamic vehicular *** learning(FL),which is a type of distributed learning technology,has been attracting great interest in recent years as it performs knowledge exchange among different network entities without a violation of user ***,client selection and networking scheme for enabling FL in dynamic vehicular environments,which determines the communication delay between FL clients and the central server that aggregates the models received from the clients,is still *** this paper,we propose an edge computing-based joint client selection and networking scheme for vehicular *** proposed scheme assigns some vehicles as edge vehicles by employing a distributed approach,and uses the edge vehicles as FL clients to conduct the training of local models,which learns optimal behaviors based on the interaction with *** clients also work as forwarder nodes in information sharing among network *** client selection takes into account the vehicle velocity,vehicle distribution,and the wireless link connectivity between vehicles using a fuzzy logic algorithm,resulting in an efficient learning and networking *** use computer simulations to evaluate the proposed scheme in terms of the communication overhead and the information covered in learning.

关键词： vehicular IoT federated learning client selection networking scheme

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An Auto-Parallel Method for Deep Learning Models Based on Genetic Algorithm

An Auto-Parallel Method for Deep Learning Models Based on Ge...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Yan Zeng ChengChuang Huang YiJie Ni ChunBao Zhou JiLin Zhang Jue Wang MingYao Zhou MeiTing Xue YunQuan Zhang School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Key Laboratory for Modeling and Simulation of Complex Systems Ministry of Education Hangzhou China Data Security Governance Zhejiang Engineering Research Center Hangzhou China School of ITMO Joint Institute Hangzhou Dianzi University Hangzhou China Institute of Computer Network Information Center of the Chinese Academy of Sciences Beijing China HuaWei State Key Laboratory of Computer Architecture Institute of Computing Technology of the Chinese Academy of Sciences Beijing China

As the size of datasets and neural network models increases, automatic parallelization methods for models have become a research hotspot in recent years. The existing auto-parallel methods based on machine learning or graph algorithms still have issues with search efficiency and applicability. This paper proposes an automatic parallel method based on a dual-population genetic algorithm, TGA, which transforms model partitioning and placement into an integer linear programming problem and constructs a cost model to evaluate the solution. The solution space is built using the neural network’s dataflow graph and device cluster’s topology, and the dual-population genetic algorithm is used to search for the optimal model parallel strategy. Experiments with various models show that the proposed method can improve single-step execution time by up to 42% compared to the Baechi method and up to 37.7% compared to the Hierarchical method.

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Study on the Extraction of Target Contours of Underwater Images 6th

Study on the Extraction of Target Contours of Underwater Ima...

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6th International Conference on Artificial Intelligence and Security, ICAIS 2020

作者： Tong, Luyan Wei, Fenglin Pan, Yupeng Wang, Kai Changchun Architecture and Civil Engineering College Changchun China College of Computer Science and Technology Jilin University Changchun China

ISBN: (纸本)9783030578831

Image is an important medium for human to obtain information, and the study of underwater image information plays an important role in marine scientific research. This paper studies the target contour extraction in the complex underwater environment. In this paper, we focus on the external contour calibration, and the study aims to segment the contours of many images with prominent texture, chromatic aberration, and shape differences. We also improve underwater image denoise and enhancement technology, restore target area features as much as possible, and process color channel selection with K-means algorithm. At the same time, the external contour extraction and the filling of the target area are partially optimized. Multi-scale watershed algorithm is used to improve the performance of the algorithm. Finally, the Canny and Grabcut algorithms are selected to detect the edges of the image. The experimental results verify that our method is feasible to extract the contours of fish in complex environments. © 2020, Springer Nature Switzerland AG.

关键词： K-means clustering

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DVR: Micro-Video Recommendation Optimizing Watch-Time-Gain under Duration Bias

arXiv

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arXiv 2022年

作者： Zheng, Yu Gao, Chen Ding, Jingtao Yi, Lingling Jin, Depeng Li, Yong Wang, Meng Department of Electronic Engineering Tsinghua University Beijing China WeChat Technical Architecture Department Tencent Inc. Shenzhen China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

Recommender systems are prone to be misled by biases in the data. Models trained with biased data fail to capture the real interests of users, thus it is critical to alleviate the impact of bias to achieve unbiased recommendation. In this work, we focus on an essential bias in micro-video recommendation, duration bias. Specifically, existing micro-video recommender systems usually consider watch time as the most critical metric, which measures how long a user watches a video. Since videos with longer duration tend to have longer watch time, there exists a kind of duration bias, making longer videos tend to be recommended more against short videos. In this paper, we empirically show that commonly-used metrics are vulnerable to duration bias, making them NOT suitable for evaluating micro-video recommendation. To address it, we further propose an unbiased evaluation metric, called WTG (short for Watch Time Gain). Empirical results reveal that WTG can alleviate duration bias and better measure recommendation performance. Moreover, we design a simple yet effective model named DVR (short for Debiased Video Recommendation) that can provide unbiased recommendation of micro-videos with varying duration, and learn unbiased user preferences via adversarial learning. Extensive experiments based on two real-world datasets demonstrate that DVR successfully eliminates duration bias and significantly improves recommendation performance with over 30% relative progress. Codes and datasets are released at https://***/tsinghua-fib-lab/WTG-DVR. © 2022, CC BY.

关键词： Watches

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A differential public PUF design for lightweight authentication 16

A differential public PUF design for lightweight authenticat...

引用

2021 International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design, SMACD 2021 and 16th Conference on PhD Research in Microelectronics and Electronics, PRIME 2021

作者： Duan, Shengyu Sai, Gaole School of Computer Engineering and Science Shanghai University Shanghai200444 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology China

ISBN: (纸本)9783800755899

Physical Unclonable Functions (PUFs) have emerged as a promising primitive to provide a hardware keyless security mechanism for integrated circuit applications. Public PUFs (PPUFs) address the crucial PUF vulnerability of requiring databases to store the secrete reference information. This paper investigates one of the PPUF families, differential PPUF (dPPUF), which exploits complexly interacted logic to create time gap between actual execution and simulation, and to prevent simulation attacks. Conventional dPPUFs have high costs. We observe the repressers have large impact on simulation time, but take a great proportion in current dPPUF structure. We thereby present a represser network with specially designed interconnection and shared logic, to maintain a high circuit complexity and to reduce the area cost. We show the proposed dPPUF has similar resilience over simulation attacks as the conventional design. Area cost can be reduced by up to 44%, and thus the proposed design can be used for lightweight authentication. © VDE VERLAG GMBH.

关键词： Authentication

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Prototype Model of an IoT-based Digital and Smart Door Locking System with Enhanced Security

Prototype Model of an IoT-based Digital and Smart Door Locki...

引用

International Conference on Mathematics, Actuarial science, computer science and Statistics (MACS)

作者： M. Adil Khan Kashif Nisar Ehtisham Lodhi Jawad Khan Muhammad Imran Khan Tariq Rahim Soomro Sana Nisar Department of Computer Science and Technology Chang'an University Xi'an China Victorian Institute of Technology (VIT) Adelaide South Australia The SKL for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Department Computer Science Abbottabad University of Science and Technology Havelian KP Pakistan Department of Computer Science Abdul Wali Khan University Mardan Pakistan CCSIS Institute of Business Management Karachi Pakistan Faculty of Computing and Informatics University Malaysia Sabah Jalan UMS Kota Kinabalu Sabah Malaysia

ISBN: (纸本)9781665460729

Biometric-based digital door locking system plays a significant role in providing authentication, reducing the workforce in smart homes, and building automation scenarios. This paper proposes a prototype model of an IoT-based Smart Door Locking System (SDLS) with reliable double-access authentication. The IoT and biometric technology authentication mechanism is adopted to make SDLS more secure and reachable with the alert mechanism on unauthorized access. The SDLS contains a biometric scanner, and a thumb impression is made either from a mobile or a biometric-mounted device on the door and both are connected to the centralized cloud-hosted real-time database. The proposed system was tested in real-time and has shown competitive results compared to other Radio-Frequency Identification (RFID) and password-based door locks. Hence, the overall performance efficiency of SDLS is satisfactory and provides biometric-based remote access to control and monitor door locks with the alert system on unauthorized access.

关键词： Biometrics (access control) Biological system modeling Authentication Prototypes Smart homes Real-time systems Security

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