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22nd International stuttgart Symposium, ISSYM 2022

作者： Frotscher, Ralf Beutenmüller, Frank Kirstädter, Andreas Keilhoff, Dan Reuss, Hans-Christian TWT GmbH Stuttgart Germany Institute of Communication Networks and Computer Engineering University of Stuttgart Stuttgart Germany University of Stuttgart Stuttgart Germany

ISBN: (纸本)9783658370107

The mobility of the future is not only relevant for passenger and goods traffic in large cities and on the highway. For an attractive and vital campus with intelligent infrastructure modern mobility is also an important precondition for achieveing excellent results in studies, research and teaching. This manuscript gives insights in current results of the Mobility Living Lab of the university of stuttgart where the campus is going to be redesigned by an innovative mobility concept. One inherent big challenge is the development and validation of automated driving functions for the future campus shuttle and the autonomous e-scotters which can be supported by modern simulation environments like Tronis®. Especially the connectivity between the different protagonists in the campus mobility is essential e.g. for traffic safety and infotainment. Herein we present results and plans from the corresponding research projects SMART (BMBF) and AINET-ANTILLAS (BMBF/CELTIC-NEXT). We focus on the topics connectivity, automation and simulation which have been realised in collaboration between the university of stuttgart and TWT in teaching courses. In these courses driver assistance systems are concepted, implemented and virtually validated to become employed in real scenarios in the further course of the project Mobility Living Lab. © Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2022.

关键词： Virtual environments

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Cost/Benefit Analysis of AIoT Image Sensing for Construction Safety Monitoring

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Journal of engineering, Project, and Production Management 2024年第3期14卷 1-13页

作者： Wang, Rong-Jing Yu, Wen-Der Liao, Hsien-Chou Chang, Hsien-Kuan Lim, Zi-Yi Architecture and Building Research Institute Ministry of Interior New Taipei23143 Taiwan Department of Construction Engineering Chaoyang University of Technology Taichung413310 Taiwan Department of Computer Science and Information Technology Chaoyang University of Technology Taichung413310 Taiwan Department of Information and Communication Engineering Chaoyang University of Technology Taichung413310 Taiwan

Rapid advances in deep learning and computer vision enable traditional cloud-based decision-making through edge computing with the Artificial Intelligent Internet of Things (AIoT) image sensors (AIoT-IS), thus improving the timeliness and security of image recognition. This study is indented to investigate the potential costs and benefits of AIoT-IS applications. This study summarizes AIoT-IS application scenarios for construction safety monitoring and proposes a cost/benefit analysis method for AIoT-IS implementation projects. According to the case study results, AIoT-IS achieves significant benefits, with a Net Present Value Index (NPVI) of 19.17% and a Benefit/Cost Ratio (BCR) of 4.65 as applied to construction site safety monitoring. Interviews with domain experts also provided qualitative feedback, pointing to the directions for future research. The proposed method is applicable for the decision-making of AIoT-IS adoption and the feasibility assessment of other innovative construction technologies. Copyright © Journal of engineering, Project, and Production Management (EPPM-Journal).

关键词： Cost benefit analysis

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Refinement and Empirical Side-Channel Analysis of Inner Product Masking with Robust Error Detection

Refinement and Empirical Side-Channel Analysis of Inner Prod...

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IEEE Symposium on On-Line Testing (IOLTS)

作者： Anton Maidl Maël Gay Osnat Keren Ilia Polian Institute for the Protection of Maritime Infrastructure German Aerospace Center (DLR e.V.) Bremerhaven Germany Institute of Computer Engineering and Computer Architecture University of Stuttgart Stuttgart Germany Faculty of Engineering Bar-Ilan University Ramat Gan Israel

ISBN: (数字)9798350370553

ISBN: (纸本)9798350370560

Side-channel attacks represent a significant and persistent threat to hardware security. One effective strategy for safeguarding hardware components against these attacks involves the implementation of masking schemes. Among these schemes, Inner Product Masking (IPM) has received considerable attention and analysis in prior research. Inner Product Masking with Error Detection aims to extend the security provided by IPM to Fault-Injection attacks. This can be achieved by incorporating (linear) repetition code for fault detection (IPM-FD) or by integrating a non-linear robust error detection into the scheme (IPM-RED). IPM-RED can detect (with non-zero probability) every fault regardless the number of bits it flips. However, this robustness comes with a cost, a non-linear function may leak via the physical channels more information than a linear one. This paper shows that information leakage from IPM-RED is marginal. An improved IPM-RED masking scheme is also presented, and an empirical side-channel leakage analysis of the protected Advanced Encryption Standard (AES) design utilizing the Test Vector Leakage Assessment (TVLA).

关键词： Resistance Measurement Codes Hardware security Side-channel attacks Information leakage Vectors

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iNUMAlloc: Towards Intelligent Memory Allocation for AI Accelerators with NUMA 21

iNUMAlloc: Towards Intelligent Memory Allocation for AI Acce...

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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on Big Data and Cloud Computing, 16th IEEE International Conference on Social Computing and Networking and 13th International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Xu, Yuanchao Qian, Ruyi Wang, Yida Huo, Qirun Capital Normal University College of Information Engineering Beijing China Skl of Computer Architecture Institute of Computing Technology Cas Beijing China

ISBN: (纸本)9798350329223

The amazing success of deep neural network benefits from the rise of big data. As deep learning models are becoming more scale than ever before, their requirements for memory bandwidth are growing at a tremendous pace. Some AI accelerators adopt non-uniform memory access (NUMA) architecture to mitigate this issue and hence complicate device memory allocation. Although extensive studies have been conducted on how to mitigate resource contention and reduce latency, almost all of them target on CPU-oriented NUMA systems but not on AI accelerators where memory allocation precedes task scheduling. The current memory allocator generally adopts an interleaved memory allocation strategy, which is very easy to implement but far from *** tackle this issue, this paper proposes iNUMAlloc, an intelligent memory allocator specialized for AI accelerators with NUMA architecture by combining program behavior and predictable hardware resources altogether. Preliminary evaluation shows that it can help to improve the accuracy and efficiency of memory allocation, thereby achieving stable execution time. © 2023 IEEE.

关键词： Deep neural networks

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EagerReuse: An Efficient Memory Reuse Approach for Complex Computational Graph 29

EagerReuse: An Efficient Memory Reuse Approach for Complex C...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Qian, Ruyi Cao, Bojun Gao, Mengjuan Shi, Qinwen Wang, Yida Xu, Yuanchao Huo, Qirun Qiu, Keni Capital Normal University College of Information Engineering Beijing China Institute of Computing Technology Cas Skl of Computer Architecture Beijing China

ISBN: (纸本)9798350330717

Memory reuse is a promising approach for deep neural network (DNN) to reduce memory consumption because it does not introduce any additional runtime overhead. We observe that existing memory reuse algorithms consider only the effect of an individual data feature (either tensor size or tensor lifetime) on memory reuse and ignore the relative position relationship (RPR) among tensors. As computational graphs grow slightly more complex, the mining of memory reuse becomes insufficient. To address this issue, we propose a new memory reuse algorithm - EagerReuse, which can exploit more memory reuse opportunities by analyzing RPR among tensors and reusing them as quickly as possible. We evaluated the algorithms with inference models in TensorFlow Model Garden, and the results show that the EagerReuse outperforms the state-of-the-art algorithms in three out of seven cases. For more complex computational graphs, EagerReuse can achieve better memory usage with slightly higher but acceptable overhead. © 2023 IEEE.

关键词： computational graph memory optimization memory reuse memory usage

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Accelerating Data Transfer in Dataflow architectures Through a Look-Ahead Acknowledgment Mechanism

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Journal of computer Science & Technology 2022年第4期37卷 942-959页

作者： Yu-Jing Feng De-Jian Li Xu Tan Xiao-Chun Ye Dong-Rui Fan Wen-Ming Li Da Wang Hao Zhang Zhi-Min Tang State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China Beijing Smartchip Microelectronics Technology Company Limited Beijing 100000China School of Computer and Control Engineering University of Chinese Academy of SciencesBeijing 100190China

The dataflow architecture,which is characterized by a lack of a redundant unified control logic,has been shown to have an advantage over the control-flow architecture as it improves the computational performance and power efficiency,especially of applications used in high-performance computing(HPC).Importantly,the high computational efficiency of systems using the dataflow architecture is achieved by allowing program kernels to be activated in a simultaneous ***,a proper acknowledgment mechanism is required to distinguish the data that logically belongs to different *** solutions include the tagged-token matching mechanism in which the data is sent before acknowledgments are received but retried after rejection,or a handshake mechanism in which the data is only sent after acknowledgments are ***,these mechanisms are characterized by both inefficient data transfer and increased area *** performance of the dataflow architecture depends on the efficiency of data *** order to optimize the efficiency of data transfer in existing dataflow architectures with a minimal increase in area and power cost,we propose a Look-Ahead Acknowledgment(LAA)*** accelerates the execution flow by speculatively acknowledging ahead without *** simulation analysis based on a handshake mechanism shows that our LAA increases the average utilization of computational units by 23.9%,with a reduction in the average execution time by 17.4%and an increase in the average power efficiency of dataflow processors by 22.4%.Crucially,our novel approach results in a relatively small increase in the area and power consumption of the on-chip logic of less than 0.9%.In conclusion,the evaluation results suggest that Look-Ahead Acknowledgment is an effective improvement for data transfer in existing dataflow architectures.

关键词： dataflow model control-flow model high-performance computing application data transfer power efficiency

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Optimizing quantum algorithms on bipotent architectures

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Physical Review A 2023年第2期108卷 022610-022610页

作者： Yanjun Ji Kathrin F. Koenig Ilia Polian Institute of Computer Architecture and Computer Engineering University of Stuttgart Pfaffenwaldring 47 70569 Stuttgart Germany Fraunhofer Institute for Applied Solid State Physics IAF Tullastrasse 72 79108 Freiburg Germany Faculty of Engineering University of Freiburg Georges-Köhler-Allee 101 79110 Freiburg Germany

Vigorous optimization of quantum gates has led to bipotent quantum architectures, where the optimized gates are available for some qubits but not for others. However, such gate-level improvements limit the application of user-side pulse-level optimizations, which have proven effective for quantum circuits with a high level of regularity, such as the ansatz circuit of the quantum approximate optimization algorithm (QAOA). In this paper, we investigate the trade-off between hardware-level and algorithm-level improvements on bipotent quantum architectures. Our results for various QAOA instances on two quantum computers offered by IBM indicate that the benefits of pulse-level optimizations currently outweigh the improvements due to vigorously optimized monolithic gates. Furthermore, our data indicate that the fidelity of circuit primitives is not always the best indicator for the overall algorithm performance; their gate type and schedule duration should also be taken into account. This effect is particularly pronounced for QAOA on dense portfolio optimization problems, since their transpilation requires many swap gates, for which efficient pulse-level optimization exists. Our findings provide practical guidance on optimal qubit selection on bipotent quantum architectures and suggest the need for improvements of those architectures, ultimately making pulse-level optimization available for all gate types.

关键词： Quantum algorithms Quantum benchmarking Quantum engineering Quantum gates Quantum information with hybrid systems Quantum simulation

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Study on Air-Purifying Performance of Semi-Flexible Asphalt Samples Coated with Titanium Dioxide Using Different Methods

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Journal of Renewable Materials 2022年第5期10卷 1321-1335页

作者： Shoujing Yan Yangyang Wang Fengxia Chi Xue Luo Tianjie Zhang Chenchen Xi Institute of Road Engineering Zhejiang Scientific Research Institute of TransportHangzhou310023China Department of Architecture and Civil Engineering Zhejiang UniversityHangzhou310027China Department of Computer Science Boise State UniversityBoise83725-1145USA

Titanium dioxide(TiO_(2))was recently employed to apply onto road surfaces to degrade the harmful compounds from vehicle ***,it remains a challenging task to find a highly compatible pavement type for TiO_(2)application to achieve durable and efficient air-purifying *** study proposed to coat TiO_(2)particles onto semi-flexible pavement surface and tried to investigate an optimum coating *** coating methods,including direct mixing TiO_(2)(MT)with asphalt mixture,spraying dry TiO_(2)(ST)coating and watersolution-based TiO_(2)(WT)coating on semi-flexible pavement *** achieve this objective,semi-flexible samples were prepared to evaluate and compare the performances of three coating methods by employing resistance to wearing,NO removal efficiency tests and residual texture depth *** was found that the ST method not only provided better NO degrading efficiency but also improved the resistance to wearing than the other two methods.

关键词： Titanium dioxide air-purifying performance semi-flexible pavement coating methods

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AGV indoor localization: a high fidelity positioning and map building solution based on drawstring displacement sensors

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Journal of Ambient Intelligence and Humanized Computing 2024年第4期15卷 2277-2293页

作者： Wang, Shih-Yuan Li, Che-Ming Liong, Sze-Teng Sheng, Yu-Ting Huang, Yen-Chang Liong, Gen-Bing Gan, Yee-Siang Graduate Institute of Architecture National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Electronic Engineering Feng Chia University Taichung40724 Taiwan School of Architecture Feng Chia University Taichung40724 Taiwan Department of Applied Mathematics National University of Tainan Tainan Taiwan Faculty of Computer Science & amp Information Technology University of Malaya Kuala Lumpur50603 Malaysia

The rapid evolution of artificial intelligence (AI) and robotics technologies are bringing drastic changes to society and industry in these recent years. The impressive progress in facilitating smart manufacturing in this era of Industry 4.0 has made our lives much more convenient than ever before. Despite the greater reliability and stability of the robotic system, there are several challenges to overcome such as the restrictions of scenes, obstacles, and hardware specifications. Since a high precision positioning algorithm is of paramount importance in devising a mobile robot, the research in developing simultaneous localization and mapping has been garnered immerse attention especially from domains of the computer vision and autonomous robots. In this paper, a novel method is presented to significantly enhance the positioning precision of indoor unmanned guided vehicles. The approach involves several steps, including setting up hardware configurations and collecting relevant data by installing necessary devices and system packages within the robot operating system (ROS). Trilateration is employed to determine the relative position of the mobile robot using distance measurements. Coordinate transformation is then conducted to convert the collected input data of relative distances and orientations. Trajectory paths are obtained, and occupancy maps are constructed to estimate the resulting trajectory and generate a 2D grid map. Indoor localization and mapping are achieved using three drawstring displacement sensors along with orientation information from an Inertial Measurement Unit (IMU). The proposed method is extensively evaluated through experimentation on predefined navigation paths, and its performance is compared to state-of-the-art methods such as RealSense T265, Hector SLAM, and wheel odometry. The results show that the proposed method exhibits compelling performance in both mean error and occupancy map construction. Ultimately, the findings reported herein

关键词： Mobile robots

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FlexPDA:A Flexible Programming Framework for Deep Learning Accelerators

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Journal of computer Science & Technology 2022年第5期37卷 1200-1220页

作者： Lei Liu Xiu Ma Hua-xiao Liu Guang-li Li Lei Liu College of Computer Science and Technology Jilin UniversityChangchunChina Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin UniversityChangchunChina State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of SciencesBeijingChina University of Chinese Academy of Sciences BeijingChina

There are a wide variety of intelligence accelerators with promising performance and energy efficiency,deployed in a broad range of applications such as computer vision and speech ***,programming productivity hinders the deployment of deep learning *** low-level library invoked in the high-level deep learning framework which supports the end-to-end execution with a given model,is designed to reduce the programming burden on the intelligence ***,it is inflexible for developers to build a network model for every deep learning application,which probably brings unnecessary repetitive *** this paper,a flexible and efficient programming framework for deep learning accelerators,FlexPDA,is proposed,which provides more optimization opportunities than the low-level library and realizes quick transplantation of applications to intelligence accelerators for fast *** evaluate FlexPDA by using 10 representative operators selected from deep learning algorithms and an end-to-end *** experimental results validate the effectiveness of FlexPDA,which achieves an end-to-end performance improvement of 1.620x over the low-level library.

关键词： deep learning accelerator programming framework domain-specific language

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