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IEEE International Conference on computer Design: VLSI in computers and Processors, (ICCD)

作者： Ning Lin Hang Lu Xing Hu Jingliang Gao Mingzhe Zhang Xiaowei Li State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Department of Electrical and Computer Engineering University of California Santa Barbara

Deep neural network (DNN) has demonstrated promising performance in various machine learning tasks. Due to the privacy issue and the unpredictable transmission latency, inferring DNN models directly on edge devices trends the development of intelligent systems, like self-driving cars, smart Internet-of-Things (IoTs) and autonomous robotics. The on-device DNN model is obtained by expensive training via vast volumes of high-quality training data in the cloud datacenter, and then deployed into these devices, expecting it to work effectively at the edge. However, edge device always deals with low-quality images caused by compression or environmental noise pollutions. The well-trained model, though could work perfectly on the cloud, cannot adapt to these edge-specific conditions without remarkable accuracy drop. In this paper, we propose an automated strategy, called "AutoMask", to embrace effective machine learning and accelerate DNN inference on edge devices. AutoMask comprises end-to-end trainable software strategies and cost-effective hardware accelerator architecture to improve the adaptability of the device without compromising the constrained computation and storage resources. Extensive experiments, over ImageNet dataset and various state-of-the-art DNNs, show that AutoMask achieves significant inference acceleration and storage reduction while maintains comparable accuracy level on embedded Xilinx Z7020 FPGA, as well as NVIDIA Jetson TX2.

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AutoFlow: Hotspot-aware, dynamic load balancing for distributed stream processing

arXiv

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arXiv 2021年

作者： Lu, Pengqi Yuan, Liang Zhang, Yunquan Cao, Hang Li, Kun State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing China

Stream applications are widely deployed on the cloud. While modern distributed streaming systems like Flink and Spark Streaming can schedule and execute them efficiently, streaming dataflows are often dynamically changing, which may cause computation imbalance and backpressure. We introduce AutoFlow, an automatic, hotspot-aware dynamic load balance system for streaming dataflows. It incorporates a centralized scheduler which monitors the load balance in the entire dataflow dynamically and implements state migrations correspondingly. The scheduler achieves these two tasks using a simple asynchronous distributed control message mechanism and a hotspot-diminishing algorithm. The timing mechanism supports implicit barriers and a highly efficient state-migration without global barriers or pauses to operators. It also supports a time-window based load-balance measurement and feeds them to the hotspot-diminishing algorithm without user interference. We implemented AutoFlow on top of Ray, an actor-based distributed execution framework. Our evaluation based on various streaming benchmark dataset shows that AutoFlow achieves good loadbalance and incurs a low latency overhead in highly data-skew workload. © 2021, CC BY.

关键词： Distributed parameter control systems

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On iterative liveness-enforcement for a class of generalized Petri nets

On iterative liveness-enforcement for a class of generalized...

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2012 IEEE International Conference on Automation Science and Engineering: Green Automation Toward a Sustainable Society, CASE 2012

作者： Hou, Yifan Liu, Ding Zhou, Mengchu School of Electro-Mechanical Engineering Xidian University No. 2 South Taibai Road Xi'an 710071 China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ 07102 United States Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 200092 China

ISBN: (纸本)9781467304283

This work presents an iterative liveness-enforcing method for a class of generalized Petri nets, which can model flexible manufacturing systems. The proposed method checks the liveness of net models using mixed integer programming and controls the token allocations of resource places instead of siphons using a liveness and resource usage ratio-enforcing supervisor. The enumeration of a kind of special structures, which is required in the previous work, is avoided and the number of iterations is bounded by the number of shared resource places in the net model. All strict minimal siphons in the controlled systems are minimally controlled. Several explanatory examples are used to illustrate this method. © 2012 IEEE.

关键词： Petri nets

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Protein-coding genes combined with long noncoding RNA as a novel transcriptome molecular staging model to predict the survival of patients with esophageal squamous cell carcinoma

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Cancer Communications 2018年第1期38卷 50-62页

作者： Jin-Cheng Guo Yang Wu Yang Chen Feng Pan Zhi-Yong Wu Jia-Sheng Zhang Jian-Yi Wu Xiu-E Xu Jian-Mei Zhao En-Min Li Yi Zhao Li-Yan Xu Key Laboratory of Molecular Biology in High Cancer Incidence Coastal Chaoshan Area of Guangdong Higher Education Institutes Shantou University Medical CollegeShantouGuangdong 515041P.R.China Department of Biochemistry and Molecular Biology Shantou University Medical CollegeShantouGuangdong 515041P.R.China Departments of Oncology Surgery Shantou Central HospitalAffiliated Shantou Hospital of Sun Yat-Sen UniversityShantouGuangdong 515041P.R.China Institute of Oncologic Pathology Shantou University Medical CollegeShantouGuangdong 515041P.R.China Key Laboratory of Intelligent Information Processing Advanced Computer Research CenterState Key Laboratory of Computer ArchitectureInstitute of Computing TechnologyChinese Academy of SciencesBeijing 100190P.R.China

Background:Esophageal squamous cell carcinoma(ESCC)is the predominant subtype of esophageal carcinoma in *** study was to develop a staging model to predict outcomes of patients with ***:Using Cox regression analysis,principal component analysis(PCA),partitioning clustering,Kaplan-Meier analysis,receiver operating characteristic(ROC)curve analysis,and classification and regression tree(CART)analysis,we mined the Gene Expression Omnibus database to determine the expression profiles of genes in 179 patients with ESCC from GSE63624 and GSE63622 ***:Univariate cox regression analysis of the GSE63624 dataset revealed that 2404 protein-coding genes(PCGs)and 635 long non-coding RNAs(lncRNAs)were associated with the survival of patients with *** categorized these PCGs and lncRNAs into three principal components(PCs),which were used to cluster the patients into three *** analysis demonstrated that the predictive ability of PCG-lncRNA PCs when applied to new patients was better than that of the tumor-node-metastasis staging(area under ROC curve[AUC]:0.69 vs.0.65,P<0.05).Accord-ingly,we constructed a molecular disaggregated model comprising one lncRNA and two PCGs,which we desig-nated as the LSB staging model using CART analysis in the GSE63624 *** LSB staging model classified the GSE63622 dataset of patients into three different groups,and its effectiveness was validated by analysis of another cohort of 105 ***:The LSB staging model has clinical significance for the prognosis prediction of patients with ESCC and may serve as a three-gene staging microarray.

关键词： Long non-coding RNA Protein-coding gene Esophageal squamous cell carcinoma Overall survival Staging model Transcriptome

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Off-path leakage power aware routing for SRAM-based FPGAs 12

Off-path leakage power aware routing for SRAM-based FPGAs

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Design, Automation and Test in Europe Conference and Exhibition

作者： Keheng Huang Yu Hu Xiaowei Li Bo Liu Hongjin Liu Jian Gong Graduate University of Chinese Academy of Sciences China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China Control Laboratory Beijing Institute of Control Engineering China

ISBN: (纸本)9783981080186

As the feature size and threshold voltage reduce, leakage power dissipation becomes an important concern in SRAM-based FPGAs. This work focuses on reducing the leakage power in routing resources, and more specifically, the leakage power dissipated in the used part of FPGA device, which is known as the active leakage power. We observe that the leakage power in off-path transistors takes up most of the active leakage power in multiplexers that control routing, and strongly depends on Hamming distance between the state of the on-path input and the states of the off-path inputs. Hence, an off-path leakage power aware routing algorithm is proposed to minimize Hamming distance between the state of on-path input and the states of off-path inputs for each multiplexer. Experimental results on MCNC benchmark circuits show that, compared with the baseline VPR technique, the proposed off-path leakage aware routing algorithm can reduce active leakage power in routing resources by 16.79%, and the increment of critical-path delay is only 1.06%.

关键词： Multiplexing Routing Wires Transistors Field programmable gate arrays Timing Vectors

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VMGuard: An Integrity Monitoring system for Management Virtual Machines

VMGuard: An Integrity Monitoring System for Management Virtu...

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International Conference on Parallel and Distributed systems (ICPADS)

作者： Haifeng Fang Yiqiang Zhao Hongyong Zang H. Howie Huang Ying Song Yuzhong Sun Zhiyong Liu University of the Chinese Academy of Sciences Beijing Beijing CN Key Laboratory of Computer System and Architecture Institute of Computing Technology Chinese Academy and Sciences China George Washington University Washington D.C. DC USA

A cloud computing provider can dynamically allocate virtual machines (VM) based on the needs of the customers, while maintaining the privileged access to the Management Virtual Machine that directly manages the hardware and supports the guest VMs. The customers must trust the cloud providers to protect the confidentiality and integrity of their applications and data. However, as the VMs from different customers are running on the same host, an attack to the management virtual machine will easily lead to the compromise of the guest VMs. Therefore, it is critical for a cloud computing system to ensure the trustworthiness of management VMs. To this end, we propose VMGuard, an integrity monitoring and detecting system for management virtual machines in a distributed environment. VMGuard utilizes a special VM, Guard Domain, which runs on each physical node to monitor the co-resident management VMs. The integrity measurements collected by the Guard Domains are sent to the VMGuard server for safe store and independent analysis. The experimental evaluation of a Xen-based prototype shows that VMGuard can quickly detect the root kit attacks while the performance overhead is low.

关键词： Driver circuits Kernel Servers Monitoring Engines Virtual machining Area measurement

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Multi-Cell Coordinated Scheduling and Power Allocation in Downlink LTE-A systems

Multi-Cell Coordinated Scheduling and Power Allocation in Do...

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IEEE Vehicular technology Conference

作者： Jian Yu Geoffrey Ye Li Changchuan Yin Suwen Tang Xiaolong Zhu Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications School of Electrical and Computer Engineering Georgia Institute of Technology Huawei Shanghai Research Institute

ISBN: (纸本)9781479944484

In this paper, we investigate multi-cell coordinated scheduling and power allocation in downlink long term evolution advanced (LTE-A) systems, where orthogonal frequency division multiple-access (OFDMA) is used. The proposed scheme performs joint scheduling, power allocation, and modulation and coding scheme (MCS) selection to maximize the overall weighted throughput with proportional fairness. Our scheme considers the practical constraints in LTE-A systems. Since the optimization problem is a combinatorial and non-convex one and is with high complexity, low-complexity and suboptimal algorithms are proposed, which separate the scheduling and power allocation into two subproblems. Simulation results show that the proposed scheme can improve the average system throughput by 10% and the 10th percentile throughput by 15% compared with the existing scheme.

关键词： Resource management Throughput Interference Signal to noise ratio Indexes Optimization Scheduling

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An Automated Feature Engineering Method for Online Payment Fraud Detection

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Jisuanji Xuebao/Chinese Journal of computers 2020年第10期43卷 1983-2001页

作者： Wang, Cheng Wang, Chang-Qi Department of Computer Science and Technology School of Electronics and Information Engineering Tongji University Shanghai201804 China The Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai201804 China Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai200092 China

Internet finance fraud is an increasingly serious social and economic problem. Online payment services (OPSs) are the typical models of Internet finance, and the fraudulent transaction in OPSs is also a typical fraud pattern. The method of identifying fraudulent transactions by constructing a fraud detection model based on machine learning has become a promising idea for online payment anti-fraud. In the process of constructing fraud detection models, the feature engineering is the most critical step. It is also one of the most time-consuming and specialized steps in the relevant area. In the study of feature engineering, the existing online payment fraud detection models are mainly carried out by experts in the form of manual construction based on business knowledge. However, there are many fraud scenarios in OPSs where the process of feature construction is so different. Artificial feature construction methods can no longer meet the increasing demand of anti-fraud. An important way to solve this problem is to automate feature engineering. In the field of Internet financial anti-fraud, the expressibility and interpretability of features play a pivotal role. It is helpful to understand the original source fields and their construction process of important features. This is useful for mining and analyzing the characteristics of fraud methods and follow-up improvement rules engines. These are of great significance for fraud detection models. Therefore, the interpretability of the model method is particularly important. Usually, the optimization of detection accuracy is carried out under the premise of ensuring interpretability. This paper proposed a lightweight, tree-structure, high efficiency and scalable automatic feature engineering method for fraud detection of online payment. The method is as follows: (1) The method has low requirements on the calculation conditions and little dependence on the dataset samples. To realize this advantage, it used the tree structur

关键词： Feature extraction

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SelectCast: Scalable Data Aggregation Scheme in Wireless Sensor Networks

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IEEE Transactions on Parallel and Distributed systems 2012年第10期23卷 1958-1969页

作者： Cheng Wang Changjun Jiang Shaojie Tang Xiang-Yang Li Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai China Department of Computer Science and Engineering University of Tongji China Department of Computer Science Illinois Institute of Technology Chicago IL USA Department of Computer Science and Engineering Tsinghua National Laboratory for Information Science and Technology (TNLIST) University of Tongji China

In this work, for a wireless sensor network (WSN) of n randomly placed sensors with node density \lambda \in [1,n], we study the tradeoffs between the aggregation throughput and gathering efficiency. The gathering efficiency refers to the ratio of the number of the sensors whose data have been gathered to the total number of sensors. Specifically, we design two efficient aggregation schemes, called single-hop-length (SHL) scheme and multiple-hop-length (MHL) scheme. By novelly integrating these two schemes, we theoretically prove that our protocol achieves the optimal tradeoffs, and derive the optimal aggregation throughput depending on a given threshold value (lower bound) on gathering efficiency. Particularly, we show that under the MHL scheme, for a practically important set of symmetric functions called divisible perfectly compressible (DPC) functions, including the mean, max, and various kinds of indicator functions, etc., the data from \Theta (n) sensors can be aggregated to the sink at the throughput of a constant order \Theta (1), implying that, our MHL scheme is indeed scalable.

关键词： Throughput Wireless sensor networks Temperature measurement Protocols Lattices Temperature sensors

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Efficient Parallelization of a Protein Sequence Comparison Algorithm on Manycore architecture

Efficient Parallelization of a Protein Sequence Comparison A...

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IEEE International Conference on Parallel and Distributed computing, Applications and Technologies (PDCAT)

作者： Xiaochun Ye Van Hoa Nguyen Dominique Lavenier Dongrui Fan Chinese Academy of Sciences Beijing China Key Lab of Computer System and Architecture Institute of Computing Technology Chinese Academy and Sciences Beijing China CNRS IRISA/INRIA Rennes France

This paper introduces the Godson-T manycore architecture and demonstrates the efficiency of its synchronization mechanism through a computation intensive bioinformatics application: the comparison of protein banks. The parallel part of the protein sequence comparison algorithm can nearly get a linear speed-up thanks to a fine tuning of the synchronization mechanism provided by the Godson-T chip.

关键词： Protein sequence Bioinformatics computer architecture Genomics Databases Tiles computer displays Scanning probe microscopy Distributed computing Application software

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