Quality of service over ATM networks

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Efficient Neural Network Implementations on Parallel Embedded Platforms Applied to Real-Time Torque-Vectoring Optimization Using Predictions for Multi-Motor Electric Vehicles

The combination of machine learning and heterogeneous embedded platforms enables new potential for developing sophisticated control concepts which are applicable to the field of vehicle dynamics and ADAS. This interdisciplinary work provides enabler solutions -ultimately implementing fast predictions using neural networks (NNs) on field programmable gate arrays (FPGAs) and graphical processing units (GPUs)- while applying them to a challenging application: Torque Vectoring on a multi-electric-motor vehicle for enhanced vehicle dynamics. The foundation motivating this work is provided by discussing multiple domains of the technological context as well as the constraints related to the automotive field, which contrast with the attractiveness of exploiting the capabilities of new embedded platforms to apply advanced control algorithms for complex control problems. In this particular case we target enhanced vehicle dynamics on a multi-motor electric vehicle benefiting from the greater degrees of freedom and controllability offered by such powertrains. Considering the constraints of the application and the implications of the selected multivariable optimization challenge, we propose a NN to provide batch predictions for real-time optimization. This leads to the major contribution of this work: efficient NN implementations on two intrinsically parallel embedded platforms, a GPU and a FPGA, following an analysis of theoretical and practical implications of their different operating paradigms, in order to efficiently harness their computing potential while gaining insight into their peculiarities. The achieved results exceed the expectations and additionally provide a representative illustration of the strengths and weaknesses of each kind of platform. Consequently, having shown the applicability of the proposed solutions, this work contributes valuable enablers also for further developments following similar fundamental principles.Some of the results presented in this work are related to activities within the 3Ccar project, which has received funding from ECSEL Joint Undertaking under grant agreement No. 662192. This Joint Undertaking received support from the European Union’s Horizon 2020 research and innovation programme and Germany, Austria, Czech Republic, Romania, Belgium, United Kingdom, France, Netherlands, Latvia, Finland, Spain, Italy, Lithuania. This work was also partly supported by the project ENABLES3, which received funding from ECSEL Joint Undertaking under grant agreement No. 692455-2

Superscalar RISC-V Processor with SIMD Vector Extension

With the increasing number of digital products in the market, the need for robust and highly configurable processors rises. The demand is convened by the stable and extensible open-sourced RISC-V instruction set architecture. RISC-V processors are becoming popular in many fields of applications and research. This thesis presents a dual-issue superscalar RISC-V processor design with dynamic execution. The proposed design employs the global sharing scheme for branch prediction and Tomasulo algorithm for out-of-order execution. The processor is capable of speculative execution with five checkpoints. Data flow in the instruction dispatch and commit stages is optimized to achieve higher instruction throughput. The superscalar processor is extended with a customized vector instruction set of single-instruction-multiple-data computations to specifically improve the performance on machine learning tasks. According to the definition of the proposed vector instruction set, the scratchpad memory and element-wise arithmetic units are implemented in the vector co-processor. Different test programs are evaluated on the fully-tested superscalar processor. Compared to the reference work, the proposed design improves 18.9% on average instruction throughput and 4.92% on average prediction hit rate, with 16.9% higher operating clock frequency synthesized on the Intel Arria 10 FPGA board. The forward propagation of a convolution neural network model is evaluated by the standalone superscalar processor and the integration of the vector co-processor. The vector program with software-level optimizations achieves 9.53× improvement on instruction throughput and 10.18× improvement on real-time throughput. Moreover, the integration also provides 2.22× energy efficiency compared with the superscalar processor along

An Analytics Platform for Integrating and Computing Spatio-Temporal Metrics

In large-scale context-aware applications, a central design concern is capturing, managing and acting upon location and context data. The ability to understand the collected data and define meaningful contextual events, based on one or more incoming (contextual) data streams, both for a single and multiple users, is hereby critical for applications to exhibit location- and context-aware behaviour. In this article, we describe a context-aware, data-intensive metrics platform —focusing primarily on its geospatial support—that allows exactly this: to define and execute metrics, which capture meaningful spatio-temporal and contextual events relevant for the application realm. The platform (1) supports metrics definition and execution; (2) provides facilities for real-time, in-application actions upon metrics execution results; (3) allows post-hoc analysis and visualisation of collected data and results. It hereby offers contextual and geospatial data management and analytics as a service, and allow context-aware application developers to focus on their core application logic. We explain the core platform and its ecosystem of supporting applications and tools, elaborate the most important conceptual features, and discuss implementation realised through a distributed, micro-service based cloud architecture. Finally, we highlight possible application fields, and present a real-world case study in the realm of psychological health

Advancing Smart Manufacturing in Europe: Experiences from Two Decades of Research and Innovation Projects

In the past two decades, a large amount of attention has been devoted to the introduction of smart manufacturing concepts and technologies into industrial practice. In Europe, these efforts have been supported by European research and innovation programs, bringing together research and application parties. In this paper, we provide an overview of a series of four content-wise connected projects on the European scale that are aimed at advancing smart manufacturing, with a focus on connecting processes on smart factory shop floors to manufacturing equipment on the one hand and enterprise-level business processes on the other hand. These projects cover several tens of application cases across Europe. We present our experiences in the form of a single, informal longitudinal case study, highlighting both the major advances and the current limitations of developments. To organize these experiences, we place them in the context of the well-known RAMI4.0 reference framework for Industry 4.0 (covering the ISA-95 standard). Then, we analyze the experiences, both the positive ones and those including problems, and draw our learnings from these. In doing so, we do not present novel technological developments in this paper—these are presented in the papers we refer to—but concentrate on the main issues we have observed to guide future developments in research efforts and industrial innovation in the smart industry domain

A Server-Based Mobile Coaching System

A prototype system for monitoring, transmitting and processing performance data in sports for the purpose of providing feedback has been developed. During training, athletes are equipped with a mobile device and wireless sensors using the ANT protocol in order to acquire biomechanical, physiological and other sports specific parameters. The measured data is buffered locally and forwarded via the Internet to a server. The server provides experts (coaches, biomechanists, sports medicine specialists etc.) with remote data access, analysis and (partly automated) feedback routines. In this way, experts are able to analyze the athlete’s performance and return individual feedback messages from remote locations

Implementation and Evaluation of Activity-Based Congestion Management Using P4 (P4-ABC)

Activity-Based Congestion management (ABC) is a novel domain-based QoS mechanism providing more fairness among customers on bottleneck links. It avoids per-flow or per-customer states in the core network and is suitable for application in future 5G networks. However, ABC cannot be configured on standard devices. P4 is a novel programmable data plane specification which allows defining new headers and forwarding behavior. In this work, we implement an ABC prototype using P4 and point out challenges experienced during implementation. Experimental validation of ABC using the P4-based prototype reveals the desired fairness results

The International Image Interoperability Framework (IIIF): raising awareness of the user benefits for scholarly editions

The International Image Interoperability Framework (IIIF), an initiative born in 2011, defines a set of common application programming interfaces (APIs) to retrieve, display, manipulate, compare, and annotate digitised and born-digital images. Upon implementation, these technical specifications have offered institutions and end users alike new possibilities. In Switzerland, only a handful of organizations and projects have collaborated with the IIIF community. For instance, e-codices, the Virtual Manuscript Library, implemented in December 2014 the two core IIIF APIs (Image API and Presentation API). Since then, no other Swiss collection has fully complied with the IIIF specifications to make true interoperability possible. The NIE-INE project, overseen by the University of Basel and funded by Swissuniversities, has aimed to build a national platform for scientific editions. There is a shared rationale between NIE-INE and IIIF who both advocate flexible and consistent technical architecture as well as providing high-quality user experience (UX) in their content delivery. Remote and in-person usability tests were conducted on the Universal Viewer (UV) and Mirador, two IIIF-compliant image viewers deployed by many IIIF implementers, in order to assess their satisfaction and efficiency as well as their perceived usability. NIE-INE was the target audience of the usability testing with a view to evaluating how scholarly research and the wider scientific community could benefit from leveraging IIIF-compliant technology. To conclude this bachelor’s thesis, a set of recommendations, based on the usability testing results and throughout this assignment, was drawn for the developing teams of both viewers, the IIIF community and the NIE-INE team members