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2023 IEEE International Radar Conference, RADAR 2023

作者： Rother, Niklas Matzner, Leonard Jaaskelainen, Pekka Leppanen, Topi Schleusner, Jens Blume, Holger Leibniz Universität Hannover Institute of Microeletronic Systems Hannover Germany Tampere University Customized Parallel Computing Group Tampere Finland

ISBN: (纸本)9781665482783

Live SAR imaging from small UAVs is an emerging field. On-board processing of the radar data requires high-performance and energy-efficient platforms. One candidate for this are Transport Triggered Architecture (TTA) processors. We implement Backprojection and Backprojection Autofocus on a TTA processor specially adapted for this task using OpenCL. The resulting implementation is compared to other platforms in terms of energy efficiency. We find that the TTA is on-par with embedded GPUs and surpasses other OpenCL-based platforms. It is outperformed only by a dedicated FPGA implementation. © 2023 IEEE.

关键词： Energy efficiency

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Synthetic Aperture Radar Algorithms on Transport Triggered Architecture Processors using OpenCL

Synthetic Aperture Radar Algorithms on Transport Triggered A...

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IEEE International Conference on Radar

作者： Niklas Rother Leonard Mätzner Pekka Jääskeläinen Topi Leppänen Jens Schleusner Holger Blume Institute of Microeletronic Systems Leibniz Universität Hannover Hannover Germany Customized Parallel Computing Group Tampere University Tampere Finland

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Let Robots Watch Grass Grow: Optimal Task Assignment for Automatic Plant Factory

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable computing 2024年第03期10卷 464-474页

作者： Xiang, Zhengzhe Xue, Xizi Chen, Yuanyi Dustdar, Schahram Guo, Minyi Hangzhou City University Hangzhou China Zhejiang University Hangzhou China Austria and part-time ICREA Research Professor at UPF Distributed Systems Group of TU Wien Barcelona Spain Shanghai Jiao Tong University Emerging Parallel Computing Center Shanghai China

Modularized plant factories, characterized by machines executing intelligent control requests to automatically take care of crops, have emerged as a sustainable agricultural paradigm, garnering the attention of Internet-of-Things and agricultural researchers for their production stability and energy efficiency. However, the diversity and pluralism of the plant factory components make it difficult to cooperate and produce crops with better qualities. Therefore, appropriate resource allocation and task scheduling strategies become the key points to optimize the quality of production in the factories by immediately telling which component is more suitable to do what in taking care of the crops. To address this challenge, this paper investigates how the machines of the factory can use their unique services and resource to help improve the crops' quality and model the machine cooperation as an online decision-making problem. An α-competitive approach called OnATS is designed based on the transformation of the original problem, and the experiments show that the proposed algorithm is superior to the baselines. Additionally, this paper explores the impact of different system configurations on the proposed method and shows that the proposed approach has broad applicability. © 2016 IEEE.

关键词： Plant Factory Resource Allocation Service Deployment Task Assignment

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On Achieving Privacy-Preserving State-of-the-Art Edge Intelligence

arXiv

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arXiv 2023年

作者： Chabal, Daphnee Sapra, Dolly Mann, Zoltán Ádám Complex Cyber Infrastructure group University of Amsterdam Lab42 Science Park 900 Amsterdam1012 WX Netherlands Parallel Computing Systems group University of Amsterdam Lab42 Science Park 900 Amsterdam1012 WX Netherlands

Deep Neural Network (DNN) Inference in Edge computing, often called Edge Intelligence, requires solutions to insure that sensitive data confidentiality and intellectual property are not revealed in the process. Privacy-preserving Edge Intelligence is only emerging, despite the growing prevalence of Edge computing as a context of Machine-Learning-as-a-Service. Solutions are yet to be applied, and possibly adapted, to state-of-the-art DNNs. This position paper provides an original assessment of the compatibility of existing techniques for privacy-preserving DNN Inference with the characteristics of an Edge computing setup, highlighting the appropriateness of secret sharing in this context. We then address the future role of model compression methods in the research towards secret sharing on DNNs with state-of-the-art performance. © 2023, CC BY-NC-SA.

关键词： Deep neural networks

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Strong consistency is not hard to get: Twophase locking and twophase commit on thousands of cores 46th

Strong consistency is not hard to get: Twophase locking and ...

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46th International Conference on Very Large Data Bases, VLDB 2020

作者： Barthels, Claude Müller, Ingo Taranov, Konstantin Alonso, Gustavo Hoefler, Torsten Systems Group Department of Computer Science ETH Zurich Switzerland Scalable Parallel Computing Lab Department of Computer Science ETH Zurich Switzerland

Concurrency control is a cornerstone of distributed database engines and storage systems. In pursuit of scalability, a common assumption is that Two-Phase Locking (2PL) and Two-Phase Commit (2PC) are not viable solutions due to their communication overhead. Recent results, however, have hinted that 2PL and 2PC might not have such a bad performance. Nevertheless, there has been no attempt to actually measure how a state-of-the-art implementation of 2PL and 2PC would perform on modern hardware. The goal of this paper is to establish a baseline for concurrency control mechanisms on thousands of cores connected through a lowlatency network. We develop a distributed lock table supporting all the standard locking modes used in database engines. We focus on strong consistency in the form of strict serializability implemented through strict 2PL, but also explore read-committed and repeatableread, two common isolation levels used in many *** do not leverage any known optimizations in the locking or commit parts of the protocols. The surprising result is that, for TPC-C, 2PL and 2PC can be made to scale to thousands of cores and hundreds of machines, reaching a throughput of over 21 million transactions per second with 9.5 million New Order operations per second. Since most existing relational database engines use some form of locking for implementing concurrency control, our findings provide a path for such systems to scale without having to significantly redesign transaction management. To achieve these results, our implementation relies on Remote Direct Memory Access (RDMA). Today, this technology is commonly available on both Infiniband as well as Ethernet networks, making the results valid across a wide range of systems and platforms, including database appliances, data centers, and cloud environments. © VLDB Endowment.

关键词： Concurrency control

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Correction to: Self-tuning serverless task farming using proactive elasticity control

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Cluster computing 2021年第4期24卷 3855-3855页

作者： Kehrer, Stefan Zietlow, Dominik Scheffold, Jochen Blochinger, Wolfgang Parallel and Distributed Computing Group Reutlingen University Reutlingen Germany Autonomous Learning Group Max-Planck-Institute for Intelligent Systems Tübingen Germany

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SkyCastle: A Resource-Aware Multi-Loop Scheduler for High-Level Synthesis

SkyCastle: A Resource-Aware Multi-Loop Scheduler for High-Le...

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IEEE International Conference on Field-Programmable Technology (FPT)

作者： Julian Oppermann Lukas Sommer Lukas Weber Melanie Reuter-Oppermann Andreas Koch Oliver Sinnen Embedded Systems and Applications Group Technische Universität Darmstadt Germany Discrete Optimization and Logistics Group Karlsruhe Institute of Technology Germany Parallel and Reconfigurable Computing Lab University of Auckland New Zealand

A common optimisation problem in the high-level synthesis (HLS) of FPGA-based accelerators is to find a microarchitecture that maximises the performance while keeping the utilisation of the device's low-level resources below certain limits. We propose to tackle it directly as part of the HLS scheduler. To that end, we formalise a general, integrated scheduling and allocation problem for HLS kernels, and present SkyCastle, a novel resource-aware multi-loop scheduler using integer linear programming to solve it for a subclass of kernels composed of multiple, nested loops. In order to demonstrate the practical applicability of the approach, we model the scheduler in such a way as to be plug-in compatible with the Xilinx Vivado HLS engine, allowing the computed solutions to be fed back into its synthesis flow. We evaluate SkyCastle for three non-trivial kernels from the machine learning, signal processing, and physical simulation domains, on two FPGA devices. Additionally, we investigate the replication of slightly slower, but smaller accelerators as a means to further boost the overall performance. In contrast to Vivado HLS' default settings, which aim at maximum performance but may fail in later synthesis steps, the solutions computed by our scheduler always result in synthesisable designs.

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Measuring success for a future vision: Defining impact in science gateways/virtual research environments

Measuring success for a future vision: Defining impact in sc...

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作者： Calyam, Prasad Wilkins-Diehr, Nancy Miller, Mark Brookes, Emre H. Arora, Ritu Chourasia, Amit Jennewein, Douglas M. Nandigam, Viswanath Drew LaMar, M. Cleveland, Sean B. Newman, Greg Wang, Shaowen Zaslavsky, Ilya Cianfrocco, Michael A. Ellett, Kevin Tarboton, David Jeffery, Keith G. Zhao, Zhiming González-Aranda, Juan Perri, Mark J. Tucker, Greg Candela, Leonardo Kiss, Tamas Gesing, Sandra Department of Electrical Engineering and Computer Science University of Missouri ColumbiaMO United States San Diego Supercomputer Center University of California San DiegoCA United States Department of Biochemistry University of Texas Health Science Center HoustonTX United States Texas Advanced Computing Center University of Texas at Austin AustinTX United States Information Technology Services University of South Dakota VermillionSD United States Biology Department College of William and Mary WilliamsburgVA United States Information Technology Services–Cyberinfrastructure University of Hawaii HonoluluHI United States Natural Resource Ecology Laboratory Colorado State University Fort CollinsCO United States Department of Geography and Geographic Information Science University of Illinois UrbanaIL United States Department of Biological Chemistry and Life Sciences Institute University of Michigan Ann ArborMI United States Pervasive Technology Institute Indiana University BloomingtonIN United States Civil and Environmental Engineering Utah State University LoganUT United States Keith G. Jeffery Consultants Faringdon United Kingdom Multiscale Networked Systems Group University of Amsterdam Amsterdam Netherlands Chief Technology Office LifeWatch ERIC Seville Spain Chemistry Department Sonoma State University Rohnert ParkCA United States Department of Geological Sciences CIRES University of Colorado Boulder BoulderCO United States National Research Council of Italy Pisa Italy Research Center for Parallel Computing University of Westminster London United Kingdom Center for Research Computing University of Notre Dame Notre DameIN United States

Scholars worldwide leverage science gateways/virtual research environments (VREs) for a wide variety of research and education endeavors spanning diverse scientific fields. Evaluating the value of a given science gateway/VRE to its constituent community is critical in obtaining the financial and human resources necessary to sustain operations and increase adoption in the user community. In this article, we feature a variety of exemplar science gateways/VREs and detail how they define impact in terms of, for example, their purpose, operation principles, and size of user base. Further, the exemplars recognize that their science gateways/VREs will continuously evolve with technological advancements and standards in cloud computing platforms, web service architectures, data management tools and cybersecurity. Correspondingly, we present a number of technology advances that could be incorporated in next-generation science gateways/VREs to enhance their scope and scale of their operations for greater success/impact. The exemplars are selected from owners of science gateways in the Science Gateways Community Institute (SGCI) clientele in the United States, and from the owners of VREs in the International Virtual Research Environment Interest group (VRE-IG) of the Research Data Alliance. Thus, community-driven best practices and technology advances are compiled from diverse expert groups with an international perspective to envisage futuristic science gateway/VRE innovations. © 2020 John Wiley & Sons, Ltd.

关键词： Web services

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Work in Progress: GeMS: A Generator for Modulo Scheduling Problems

Work in Progress: GeMS: A Generator for Modulo Scheduling Pr...

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Proceedings of the International Conference on Compilers, Architectures and Synthesis for Embedded systems (CASES)

作者： Julian Oppermann Sebastian Vollbrecht Melanie Reuter-Oppermann Oliver Sinnen Andreas Koch Embedded Systems and Applications Group Technische Universität Darmstadt Germany Discrete Optimization and Logistics Group Karlsruhe Service Research Institute Karlsruhe Institute of Technology Germany Parallel and Reconfigurable Computing Lab University of Auckland New Zealand

GeMS is a customisable, open-source toolkit for generating random, yet constrained, modulo scheduling problems with a known optimal initiation interval. These can then be used to evaluate the behavior of different sch... 详细信息

关键词： Processor scheduling Schedules Generators Kernel Computer architecture Runtime Embedded systems

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Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients

IEEE Transactions on Technology and Society

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IEEE Transactions on Technology and Society 2022年第4期3卷 272-289页

作者： Allahabadi, Himanshi Amann, Julia Balot, Isabelle Beretta, Andrea Binkley, Charles Bozenhard, Jonas Bruneault, Frederick Brusseau, James Candemir, Sema Cappellini, Luca Alessandro Chakraborty, Subrata Cherciu, Nicoleta Cociancig, Christina Coffee, Megan Ek, Irene Espinosa-Leal, Leonardo Farina, Davide Fieux-Castagnet, Genevieve Frauenfelder, Thomas Gallucci, Alessio Giuliani, Guya Golda, Adam Van Halem, Irmhild Hildt, Elisabeth Holm, Sune Kararigas, Georgios Krier, Sebastien A. Kuhne, Ulrich Lizzi, Francesca Madai, Vince I. Markus, Aniek F. Masis, Serg Mathez, Emilie Wiinblad Mureddu, Francesco Neri, Emanuele Osika, Walter Ozols, Matiss Panigutti, Cecilia Parent, Brendan Pratesi, Francesca Moreno-Sanchez, Pedro A. Sartor, Giovanni Savardi, Mattia Signoroni, Alberto Sormunen, Hanna-Maria Spezzatti, Andy Srivastava, Adarsh Stephansen, Annette F. Theng, Lau Bee Tithi, Jesmin Jahan Tuominen, Jarno Umbrello, Steven Vaccher, Filippo Vetter, Dennis Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Ey Netherlands Enterprise Intelligence Department Amsterdam1083 HP Netherlands Eth Zurich Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich8092 Switzerland Center for Diplomatic and Strategic Studies Postgraduate Studies in Diplomacy and International Relations Paris75015 France Pisa56124 Italy Hackensack Meridian Health Bioethics Center EdisonNJ08820 United States University of Oxford Faculty of Philosophy OxfordOX2 6GG United Kingdom Collège André- Laurendeau Philosophie Department MontrealQCH8N 2J4 Canada Université du Québec À Montréal École des Médias MontrealQCH2L 2C4 Canada Pace University Philosophy Department New YorkNY10038 United States The Ohio State University Wexner Medical Center Department of Radiology ColumbusOH43210 United States Humanitas Research Hospital Department of Radiology Milan20089 Italy Humanitas University Department of Biomedical Sciences Milan20089 Italy University of New England Faculty of Science Agriculture Business and Law ArmidaleNSW2351 Australia University of Technology Sydney Faculty of Engineering and Information Technology SydneyNSW2007 Australia Scuola Superiore Sant'Anna European Centre of Excellence on the Regulation of Robotics and Ai Pisa56127 Italy University of Bremen Group of Computer Architecture Bremen28359 Germany New York University Grossman School of Medicine Division of Infectious Diseases and Immunology Department of Medicine New YorkNY10016 United States Digital Institute Ai Research Section Stockholm16731 Sweden Arcada University of Applied Sciences Department of Business Management and Analytics Helsinki00550 Finland University of Brescia Radiological Sciences and Public Health Department of Medical and Surgical Specialties Brescia25121 Italy Sncf Reseau Sa Ethique Groupe La Plaine93418 France Institute of Diagnostic and Interventional Radiology University Hospital Zurich Zürich8091 Switzerland Eindhoven University of Tech

This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert group's (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare and 2) to investigate the research question of what does 'trustworthy AI' mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multiregional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient's lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia, Italy, since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses sociotechnical scenarios to identify ethical, technical, and domain-specific issues in the use of the AI system in the context of the pandemic. © 2020 IEEE.

关键词： COVID-19

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