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An optimal multicast-based testing algorithm for hotspot avoidance in NoC-based many-core chips

Harbin Gongye Daxue Xuebao/Journal of Harbin institute of technology 2009年第SUPPL. 1期41卷 166-171页

作者： Fang, Fang Wang, Wei Han, Yin-He Li, Xiao-Wei Hefei University of Technology Hefei 230009 China System Architecture Key Laboratory Chinese Academy of Sciences Beijing 100190 China Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China

When many-core chips based on Network-on-chip (NoC) are under test, test time can be much reduced by using multicasting communication as data transmission technology and achieving parallel testing of many identical cores. However, it may have high power density and cause hotspot in test, which can exacerbate crosstalk and delay problems. In this paper, we present an optimal multicast-based testing algorithm for hotspot avoidance by generating multicasting test paths. Considering the thermal factor, we further investigate the paths, analyze the reason of hotspot and the effect of cores' distribution on hotspot, and propose a multicasting paths optimization strategy which is designed to avoid hotspot within identical cores. Experimental results show that the temperature of the many-core chip can be restricted and hotspots can be avoided efficiently.

关键词： Network-on-chip

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BugListener: Identifying and Synthesizing Bug Reports from Collaborative Live Chats

BugListener: Identifying and Synthesizing Bug Reports from C...

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International Conference on Software Engineering (ICSE)

作者： Lin Shi Fangwen Mu Yumin Zhang Ye Yang Junjie Chen Xiao Chen Hanzhi Jiang Ziyou Jiang Qing Wang Laboratory for Internet Software Technologies Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China School of Systems and Enterprises Stevens Institute of Technology Hoboken NJ USA College of Intelligence and Computing Tianjin University Tianjin China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China Science &#x0026 Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665495899

In community-based software development, developers frequently rely on live-chatting to discuss emergent bugs/errors they encounter in daily development tasks. However, it remains a challenging task to accurately record such knowledge due to the noisy nature of interleaved dialogs in live chat data. In this paper, we first formulate the task of identifying and synthesizing bug reports from commu-nity live chats, and propose a novel approach, named BugListener, to address the challenges. Specifically, BugListener automates three sub-tasks: 1) Disentangle the dialogs from massive chat logs by using a Feed-Forward neural network; 2) Identify the bug-report dialogs from separated dialogs by leveraging the Graph neural net-work to learn the contextual information; 3) Synthesize the bug reports by utilizing Transfer Learning techniques to classify the sentences into: observed behaviors (OB), expected behaviors (EB), and steps to reproduce the bug (SR). BugListener is evaluated on six open source projects. The results show that: for bug report identification, BugListener achieves the average Fl of 77.74%, im-proving the best baseline by 12.96%; and for bug report synthesis task, BugListener could classify the OB, EB, and SR sentences with the F1 of 84.62%, 71.46%, and 73.13%, improving the best baselines by 9.32%,12.21%,10.91%, respectively. A human evaluation study also confirms the effectiveness of Bug Listener in generating relevant and accurate bug reports. These demonstrate the significant potential of applying BugListener in community-based software development, for promoting bug discovery and quality improvement.

关键词： computer bugs Transfer learning Neural networks Collaboration Predictive models Software Behavioral sciences

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BugListener: Identifying and Synthesizing Bug Reports from Collaborative Live Chats

arXiv

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arXiv 2022年

作者： Shi, Lin Mu, Fangwen Zhang, Yumin Yang, Ye Chen, Junjie Chen, Xiao Jiang, Hanzhi Jiang, Ziyou Wang, Qing Laboratory for Internet Software Technologies Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China Science & Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences Beijing China School of Systems and Enterprises Stevens Institute of Technology HobokenNJ United States Tianjin University College of Intelligence and Computing Tianjin China

In community-based software development, developers frequently rely on live-chatting to discuss emergent bugs/errors they encounter in daily development tasks. However, it remains a challenging task to accurately record such knowledge due to the noisy nature of interleaved dialogs in live chat data. In this paper, we first formulate the task of identifying and synthesizing bug reports from community live chats, and propose a novel approach, named BugListener, to address the challenges. Specifically, BugListener automates three sub-tasks: 1) Disentangle the dialogs from massive chat logs by using a Feed-Forward neural network;2) Identify the bug-report dialogs from separated dialogs by leveraging the Graph neural network to learn the contextual information;3) Synthesize the bug reports by utilizing Transfer Learning techniques to classify the sentences into: observed behaviors (OB), expected behaviors (EB), and steps to reproduce the bug (SR). BugListener is evaluated on six open source projects. The results show that: for bug report identification, BugListener achieves the average F1 of 77.74%, improving the best baseline by 12.96%;and for bug report synthesis task, BugListener could classify the OB, EB, and SR sentences with the F1 of 84.62%, 71.46%, and 73.13%, improving the best baselines by 9.32%, 12.21%, 10.91%, respectively. A human evaluation study also confirms the effectiveness of BugListener in generating relevant and accurate bug reports. These demonstrate the significant potential of applying BugListener in community-based software development, for promoting bug discovery and quality improvement. Copyright © 2022, The Authors. All rights reserved.

关键词： Software design

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SPECBOX: A label-based transparent speculation scheme against transient execution attacks

arXiv

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arXiv 2021年

作者： Tang, Bowen Wu, Chenggang Wang, Zhe Jia, Lichen Yew, Pen-Chung Cheng, Yueqiang Zhang, Yinqian Wang, Chenxi Xu, Guoqing Harry State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China University of the Chinese Academy of Sciences China University of Minnesota Twin Cities United States NIO Security Research United States Southern University of Science and Technology China University of California Los Angeles United States

Speculative execution techniques have been a cornerstone of modern processors to improve instruction-level parallelism. However, recent studies showed that this kind of techniques could be exploited by attackers to leak secret data via transient execution attacks, such as Spectre. Many defenses are proposed to address this problem, but they all face various challenges: (1) Tracking data flow in the instruction pipeline could comprehensively address this problem, but it could cause pipeline stalls and incur high performance overhead;(2) Making side effect of speculative execution imperceptible to attackers, but it often needs additional storage components and complicated data movement operations. In this paper, we propose a label-based transparent speculation scheme called SPECBOX. It dynamically partitions the cache system to isolate speculative data and non-speculative data, which can prevent transient execution from being observed by subsequent execution. Moreover, it uses thread ownership semaphores to prevent speculative data from being accessed across cores. In addition, SPECBOX also enhances the auxiliary components in the cache system against transient execution attacks, such as hardware prefetcher. Our security analysis shows that SPECBOX is secure and the performance evaluation shows that the performance overhead on SPEC CPU 2006 and PARSEC-3.0 benchmarks is small. Copyright © 2021, The Authors. All rights reserved.

关键词： Digital storage

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Implementation and performance analysis of SDN controller

引用

Dongbei Daxue Xuebao/Journal of Northeastern University 2014年 35卷 313-318页

作者： Jiang, Guo-Long Wu, Jie Liu, Ke Fu, Bin-Zhang Chen, Ming-Yu State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100190 China

Based on the Beacon controller, this article first analyzed the implementation of the core functions of Beacon, including the switches connection, topology discovery and routing path calculation. Second, we evaluated the core functions in the controller using the Mininet Simulator and Open Switch testbed. At last, based on the routing path calculation result, we proposed a new method, named routing pool, to improve the performance. When the controller calculates the shortest path of all pairs, it calculates another path for the pair. Then the controller can push routing path directly when link failure occurs in the network, which can decrease the cost of path calculation. The results show some new light on the design and optimization for SDN controllers. ©, 2014, Northeastern University. All right reserved.

关键词： Controllers

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Incorporation of Ordinal Optimization into Learning Automata for High Learning Efficiency

Incorporation of Ordinal Optimization into Learning Automata...

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IEEE International Conference on Automation Science and Engineering

作者： JunQi Zhang Cheng Wang Di Zang MengChu Zhou Department of Computer Science and Technology the Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Department of Electrical and Computer Engineering New Jersey Institute of Technology

ISBN: (纸本)9781467381840

Learning automata (LA) represent important leaning mechanisms with applications in automated system design, biological system modeling, computer vision, and transportation. They play the critical roles in modeling a process as well as generating the appropriate signal to control it. They update their action probabilities in accordance with the inputs received from the environment and can improve their own performance during operations. The action probability vector in LA takes charge of two functions: 1) The cost of convergence, i.e., the size of sampling budget;2) The allocation of sampling budget among actions to identify the optimal one. These two intertwined functions lead to a problem: The sampling budget mostly goes to the currently estimated optimal action due to its high action probability regardless whether it can help identify the real optimal action or not. This work proposes a new class of LA that separates the allocation of sampling budget from the action probability vector. It uses the action probability vector to determine the size of sampling budget and then uses Optimal computing Budget Allocation (OCBA) to accomplish the allocation of sampling budget in a way that maximizes the probability of identifying the true optimal action. Simulation results verify its significant speedup ranging from 10.93% to 65.94% over the best existing LA algorithms.

关键词： Convergence Optimization Resource management Learning automata Computational modeling Pursuit algorithms Accuracy

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CFDIL: A context-aware feature deep interaction learning for app recommendation

Research Square

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Research Square 2021年

作者： Hao, Qingbo Zhu, Ke Wang, Chundong Wang, Peng Mo, Xiuliang Liu, Zhen Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin China School of Computer Science and Engineering Tianjin University of Technology Tianjin China Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin China Graduate School of Engineering Nagasaki Institute of Applied Science Nagasaki Japan

The rapid development of Mobile Internet has spawned various mobile applications (apps). A large number of apps make it difficult for users to choose apps conveniently, causing the app overload problem. As the most effective tool to solve the problem of app overload, the app recommendation has attracted extensive attention of researchers. Traditional recommendation methods usually use historical data of apps used by users to explore their preferences, and then make an app recommendation list for users. Although the traditional app recommendation methods have achieved certain results, the performance of app recommendation still needs to be improved due to the following two reasons. On the one hand, it is difficult to construct traditional app recommendation models when facing with the sparse user-app interaction data. On the other hand, contextual information has a large impact on users’ app usage preferences, which is often overlooked by traditional app recommendation methods. To overcome the aforementioned problems, we proposed a Context-aware Feature Deep Interaction Learning (CFDIL) method to explore user preferences, and then perform app recommendation by learning potential user-app relationships in different contexts. The novelty of CFDIL is as follows: (1) CFDIL incorporates contextual features into users’ preferences modeling by constructing a novel user and app feature portrait. (2) The problem of data sparsity is effectively solved by the use of dense user and app feature portraits, as well as the tensor operations for label sets. (3) CFDIL trains a new deep network structure, which can make accurate app recommendation using the contextual information and attribute information of users and apps. We applied CFDIL on three real datasets and conducted extensive experiments, which showed that CFDIL outperformed the benchmark method. © 2021, CC BY.

关键词： User profile

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Preface

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Journal of computer Science & technology 2021年第5期36卷 1087-1088页

作者： Hua-Wei Li State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of SciencesBeijing

In line with the development needs of China’s integrated circuit design field,the China computer Federation(CCF)established the CCF Task Force on Integrated Circuit Design(TFICD)in October *** mission of TFICD includes:organizing academic activities in the field of integrated circuit design and strengthening exchanges with international peers,promoting the interdisciplinary and vertical optimization of computer and microelectronics,and promoting the collaborations among industry,universities and research institutions.

关键词： strengthening universities institutions

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An Ultra-Low Power 3-T Chaotic Map based True Random Number Generator

An Ultra-Low Power 3-T Chaotic Map based True Random Number ...

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IEEE Asian Hardware-Oriented Security and Trust (AsianHOST)

作者： Lijuan Han Yuan Cao Lei Qian Haodong Xie Chip-Hong Chang College of Internet of Tings Engineering Hohai University Chang Zhou China Changzhou Galaxy Century Microelectronics Co. Ltd. State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences School of Electrical and Electronic Engineering Nanyang Technological University Singapore

ISBN: (纸本)9781665441865

In this paper, discrete-time chaos generator is exploited for lightweight and energy-efficient true random number generator (TRNG) implementation. The chaos generator is realized with a two-stage tent map circuit with feedback. Each stage consists of three transistors. Its transient characteristics can be tuned over a range of bias voltage to realize a family of tent map functions. The chaotic output voltage amplitude of each stage is highly sensitive to the environmental noise. The difference signal between two such chaos generators at each stage is amplified to produce a random binary output. The output of the quantized sense amplifier is sampled at twice the bit rate of each stage of tent map circuit. The proposed design was simulated using a standard 55 nm 1.2 V CMOS process. The results show that it has a very low power consumption of 10.98 μW. It has an ultra-low energy consumption of only 5.49 pJ/bit at a throughput of 2 Mbps, which is 19x lower than the recently reported state-of-the-art and the most power efficient chaotic TRNGs. The random bit stream generated by the proposed TRNG has passed entropy, ACF and NIST randomness tests.

关键词： Chaos Voltage NIST Throughput CMOS process Generators Entropy

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Aggregation capacity of wireless sensor networks: Extended network case

Aggregation capacity of wireless sensor networks: Extended n...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Cheng Wang Changjun Jiang Yunhao Liu Xiang-Yang Li Shaojie Tang Huadong Ma Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai China Department of Computer Science University of Tongji Shanghai China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong China TNLIST School of Software Tsinghua University China Department of Computer Science Illinois Institute of Technology Chicago IL USA Department of Computer Science Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781424499199

A critical function of wireless sensor networks (WSNs) is data gathering. While, one is often only interested in collecting a relevant function of the sensor measurements at a sink node, rather than downloading all the data from all the sensors. This paper studies the capacity of computing and transporting the specific functions of sensor measurements to the sink node, called aggregation capacity, for WSNs. It focuses on random WSNs that can be classified into two types: random extended WSN and random dense WSN. All existing results about aggregation capacity are studied for dense WSNs, including random cases and arbitrary cases, under the protocol model (ProM) or physical model (PhyM). In this paper, we propose the first aggregation capacity scaling laws for random extended WSNs. We point out that unlike random dense WSNs, for random extended WSNs, the assumption made in ProM and PhyM that each successful transmission can sustain a constant rate is over-optimistic and unpractical due to transmit power *** derive the first result on aggregation capacity for random extended WSNs under the generalized physical model. Particularly, we prove that, for the type-sensitive perfectly compressible functions and type-threshold perfectly compressible functions, the aggregation capacities for random extended WSNs with n nodes are of order Θ ((log n) -β/2-1 ) and Θ (((log n) -β/2 )/(log log n)), respectively, where β >; 2 denotes the power attenuation exponent in the generalized physical model.

关键词： Wireless sensor networks Signal to noise ratio Interference Throughput Lattices Routing PROM

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