118 research outputs found

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

    Get PDF
    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

    A compact high-energy particle detector for low-cost deep space missions

    Get PDF
    Over the last few decades particle physics has led to many new discoveries, laying the foundation for modern science. However, there are still many unanswered questions which the next generation of particle detectors could address, potentially expanding our knowledge and understanding of the Universe. Owing to recent technological advancements, electronic sensors are now able to acquire measurements previously unobtainable, creating opportunities for new deep-space high-energy particle missions. Consequently, a new compact instrument was developed capable of detecting gamma rays, neutrons and charged particles. This instrument combines the latest in FPGA System-on-Chip technology as the central processor and a 3x3 array of silicon photomultipliers coupled with an organic plastic scintillator as the detector. Using modern digital pulse shape discrimination and signal processing techniques, the scintillator and photomultiplier combination has been shown to accurately discriminate between the di_erent particle types and provide information such as total energy and incident direction. The instrument demonstrated the ability to capture 30,000 particle events per second across 9 channels - around 15 times that of the U.S. based CLAS detector. Furthermore, the input signals are simultaneously sampled at a maximum rate of 5 GSPS across all channels with 14-bit resolution. Future developments will include FPGA-implemented digital signal processing as well as hardware design for small satellite based deep-space missions that can overcome radiation vulnerability

    Autonomous Pedestrian Detection in Transit Buses

    Get PDF
    This project created a proof of concept for an automated pedestrian detection and avoidance system designed for transit buses. The system detects objects up to 12 meters away, calculates the distance from the system using a solid-state LIDAR, and determines if that object is human by passive infrared. This triggers a visual and sound warning. A Xilinx Zynq-SoC utilizing programmable logic and an ARM-based processing system drive data fusion, and an external power unit makes it configurable for transit-buses

    Exploiting All-Programmable System on Chips for Closed-Loop Real-Time Neural Interfaces

    Get PDF
    High-density microelectrode arrays (HDMEAs) feature thousands of recording electrodes in a single chip with an area of few square millimeters. The obtained electrode density is comparable and even higher than the typical density of neuronal cells in cortical cultures. Commercially available HDMEA-based acquisition systems are able to record the neural activity from the whole array at the same time with submillisecond resolution. These devices are a very promising tool and are increasingly used in neuroscience to tackle fundamental questions regarding the complex dynamics of neural networks. Even if electrical or optical stimulation is generally an available feature of such systems, they lack the capability of creating a closed-loop between the biological neural activity and the artificial system. Stimuli are usually sent in an open-loop manner, thus violating the inherent working basis of neural circuits that in nature are constantly reacting to the external environment. This forbids to unravel the real mechanisms behind the behavior of neural networks. The primary objective of this PhD work is to overcome such limitation by creating a fullyreconfigurable processing system capable of providing real-time feedback to the ongoing neural activity recorded with HDMEA platforms. The potentiality of modern heterogeneous FPGAs has been exploited to realize the system. In particular, the Xilinx Zynq All Programmable System on Chip (APSoC) has been used. The device features reconfigurable logic, specialized hardwired blocks, and a dual-core ARM-based processor; the synergy of these components allows to achieve high elaboration performances while maintaining a high level of flexibility and adaptivity. The developed system has been embedded in an acquisition and stimulation setup featuring the following platforms: \u2022 3\ub7Brain BioCam X, a state-of-the-art HDMEA-based acquisition platform capable of recording in parallel from 4096 electrodes at 18 kHz per electrode. \u2022 PlexStim\u2122 Electrical Stimulator System, able to generate electrical stimuli with custom waveforms to 16 different output channels. \u2022 Texas Instruments DLP\uae LightCrafter\u2122 Evaluation Module, capable of projecting 608x684 pixels images with a refresh rate of 60 Hz; it holds the function of optical stimulation. All the features of the system, such as band-pass filtering and spike detection of all the recorded channels, have been validated by means of ex vivo experiments. Very low-latency has been achieved while processing the whole input data stream in real-time. In the case of electrical stimulation the total latency is below 2 ms; when optical stimuli are needed, instead, the total latency is a little higher, being 21 ms in the worst case. The final setup is ready to be used to infer cellular properties by means of closed-loop experiments. As a proof of this concept, it has been successfully used for the clustering and classification of retinal ganglion cells (RGCs) in mice retina. For this experiment, the light-evoked spikes from thousands of RGCs have been correctly recorded and analyzed in real-time. Around 90% of the total clusters have been classified as ON- or OFF-type cells. In addition to the closed-loop system, a denoising prototype has been developed. The main idea is to exploit oversampling techniques to reduce the thermal noise recorded by HDMEAbased acquisition systems. The prototype is capable of processing in real-time all the input signals from the BioCam X, and it is currently being tested to evaluate the performance in terms of signal-to-noise-ratio improvement

    Dynamic Partial Reconfiguration for Dependable Systems

    Get PDF
    Moore’s law has served as goal and motivation for consumer electronics manufacturers in the last decades. The results in terms of processing power increase in the consumer electronics devices have been mainly achieved due to cost reduction and technology shrinking. However, reducing physical geometries mainly affects the electronic devices’ dependability, making them more sensitive to soft-errors like Single Event Transient (SET) of Single Event Upset (SEU) and hard (permanent) faults, e.g. due to aging effects. Accordingly, safety critical systems often rely on the adoption of old technology nodes, even if they introduce longer design time w.r.t. consumer electronics. In fact, functional safety requirements are increasingly pushing industry in developing innovative methodologies to design high-dependable systems with the required diagnostic coverage. On the other hand commercial off-the-shelf (COTS) devices adoption began to be considered for safety-related systems due to real-time requirements, the need for the implementation of computationally hungry algorithms and lower design costs. In this field FPGA market share is constantly increased, thanks to their flexibility and low non-recurrent engineering costs, making them suitable for a set of safety critical applications with low production volumes. The works presented in this thesis tries to face new dependability issues in modern reconfigurable systems, exploiting their special features to take proper counteractions with low impacton performances, namely Dynamic Partial Reconfiguration

    Real-time multi-domain optimization controller for multi-motor electric vehicles using automotive-suitable methods and heterogeneous embedded platforms

    Get PDF
    Los capítulos 2,3 y 7 están sujetos a confidencialidad por el autor. 145 p.In this Thesis, an elaborate control solution combining Machine Learning and Soft Computing techniques has been developed, targeting a chal lenging vehicle dynamics application aiming to optimize the torque distribution across the wheels with four independent electric motors.The technological context that has motivated this research brings together potential -and challenges- from multiple dom ains: new automotive powertrain topologies with increased degrees of freedom and controllability, which can be approached with innovative Machine Learning algorithm concepts, being implementable by exploiting the computational capacity of modern heterogeneous embedded platforms and automated toolchains. The complex relations among these three domains that enable the potential for great enhancements, do contrast with the fourth domain in this context: challenging constraints brought by industrial aspects and safe ty regulations. The innovative control architecture that has been conce ived combines Neural Networks as Virtual Sensor for unmeasurable forces , with a multi-objective optimization function driven by Fuzzy Logic , which defines priorities basing on the real -time driving situation. The fundamental principle is to enhance vehicle dynamics by implementing a Torque Vectoring controller that prevents wheel slip using the inputs provided by the Neural Network. Complementary optimization objectives are effici ency, thermal stress and smoothness. Safety -critical concerns are addressed through architectural and functional measures.Two main phases can be identified across the activities and milestones achieved in this work. In a first phase, a baseline Torque Vectoring controller was implemented on an embedded platform and -benefiting from a seamless transition using Hardware-in -the -Loop - it was integrated into a real Motor -in -Wheel vehicle for race track tests. Having validated the concept, framework, methodology and models, a second simulation-based phase proceeds to develop the more sophisticated controller, targeting a more capable vehicle, leading to the final solution of this work. Besides, this concept was further evolved to support a joint research work which lead to outstanding FPGA and GPU based embedded implementations of Neural Networks. Ultimately, the different building blocks that compose this work have shown results that have met or exceeded the expectations, both on technical and conceptual level. The highly non-linear multi-variable (and multi-objective) control problem was tackled. Neural Network estimations are accurate, performance metrics in general -and vehicle dynamics and efficiency in particular- are clearly improved, Fuzzy Logic and optimization behave as expected, and efficient embedded implementation is shown to be viable. Consequently, the proposed control concept -and the surrounding solutions and enablers- have proven their qualities in what respects to functionality, performance, implementability and industry suitability.The most relevant contributions to be highlighted are firstly each of the algorithms and functions that are implemented in the controller solutions and , ultimately, the whole control concept itself with the architectural approaches it involves. Besides multiple enablers which are exploitable for future work have been provided, as well as an illustrative insight into the intricacies of a vivid technological context, showcasing how they can be harmonized. Furthermore, multiple international activities in both academic and professional contexts -which have provided enrichment as well as acknowledgement, for this work-, have led to several publications, two high-impact journal papers and collateral work products of diverse nature

    Real-time multi-domain optimization controller for multi-motor electric vehicles using automotive-suitable methods and heterogeneous embedded platforms

    Get PDF
    Los capítulos 2,3 y 7 están sujetos a confidencialidad por el autor. 145 p.In this Thesis, an elaborate control solution combining Machine Learning and Soft Computing techniques has been developed, targeting a chal lenging vehicle dynamics application aiming to optimize the torque distribution across the wheels with four independent electric motors.The technological context that has motivated this research brings together potential -and challenges- from multiple dom ains: new automotive powertrain topologies with increased degrees of freedom and controllability, which can be approached with innovative Machine Learning algorithm concepts, being implementable by exploiting the computational capacity of modern heterogeneous embedded platforms and automated toolchains. The complex relations among these three domains that enable the potential for great enhancements, do contrast with the fourth domain in this context: challenging constraints brought by industrial aspects and safe ty regulations. The innovative control architecture that has been conce ived combines Neural Networks as Virtual Sensor for unmeasurable forces , with a multi-objective optimization function driven by Fuzzy Logic , which defines priorities basing on the real -time driving situation. The fundamental principle is to enhance vehicle dynamics by implementing a Torque Vectoring controller that prevents wheel slip using the inputs provided by the Neural Network. Complementary optimization objectives are effici ency, thermal stress and smoothness. Safety -critical concerns are addressed through architectural and functional measures.Two main phases can be identified across the activities and milestones achieved in this work. In a first phase, a baseline Torque Vectoring controller was implemented on an embedded platform and -benefiting from a seamless transition using Hardware-in -the -Loop - it was integrated into a real Motor -in -Wheel vehicle for race track tests. Having validated the concept, framework, methodology and models, a second simulation-based phase proceeds to develop the more sophisticated controller, targeting a more capable vehicle, leading to the final solution of this work. Besides, this concept was further evolved to support a joint research work which lead to outstanding FPGA and GPU based embedded implementations of Neural Networks. Ultimately, the different building blocks that compose this work have shown results that have met or exceeded the expectations, both on technical and conceptual level. The highly non-linear multi-variable (and multi-objective) control problem was tackled. Neural Network estimations are accurate, performance metrics in general -and vehicle dynamics and efficiency in particular- are clearly improved, Fuzzy Logic and optimization behave as expected, and efficient embedded implementation is shown to be viable. Consequently, the proposed control concept -and the surrounding solutions and enablers- have proven their qualities in what respects to functionality, performance, implementability and industry suitability.The most relevant contributions to be highlighted are firstly each of the algorithms and functions that are implemented in the controller solutions and , ultimately, the whole control concept itself with the architectural approaches it involves. Besides multiple enablers which are exploitable for future work have been provided, as well as an illustrative insight into the intricacies of a vivid technological context, showcasing how they can be harmonized. Furthermore, multiple international activities in both academic and professional contexts -which have provided enrichment as well as acknowledgement, for this work-, have led to several publications, two high-impact journal papers and collateral work products of diverse nature

    A portable device for time-resolved fluorescence based on an array of CMOS SPADs with integrated microfluidics

    Get PDF
    [eng] Traditionally, molecular analysis is performed in laboratories equipped with desktop instruments operated by specialized technicians. This paradigm has been changing in recent decades, as biosensor technology has become as accurate as desktop instruments, providing results in much shorter periods and miniaturizing the instrumentation, moving the diagnostic tests gradually out of the central laboratory. However, despite the inherent advantages of time-resolved fluorescence spectroscopy applied to molecular diagnosis, it is only in the last decade that POC (Point Of Care) devices have begun to be developed based on the detection of fluorescence, due to the challenge of developing high-performance, portable and low-cost spectroscopic sensors. This thesis presents the development of a compact, robust and low-cost system for molecular diagnosis based on time-resolved fluorescence spectroscopy, which serves as a general-purpose platform for the optical detection of a variety of biomarkers, bridging the gap between the laboratory and the POC of the fluorescence lifetime based bioassays. In particular, two systems with different levels of integration have been developed that combine a one-dimensional array of SPAD (Single-Photon Avalanch Diode) pixels capable of detecting a single photon, with an interchangeable microfluidic cartridge used to insert the sample and a laser diode Pulsed low-cost UV as a source of excitation. The contact-oriented design of the binomial formed by the sensor and the microfluidic, together with the timed operation of the sensors, makes it possible to dispense with the use of lenses and filters. In turn, custom packaging of the sensor chip allows the microfluidic cartridge to be positioned directly on the sensor array without any alignment procedure. Both systems have been validated, determining the decomposition time of quantum dots in 20 nl of solution for different concentrations, emulating a molecular test in a POC device.[cat] Tradicionalment, l'anàlisi molecular es realitza en laboratoris equipats amb instruments de sobretaula operats per tècnics especialitzats. Aquest paradigma ha anat canviant en les últimes dècades, a mesura que la tecnologia de biosensor s'ha tornat tan precisa com els instruments de sobretaula, proporcionant resultats en períodes molt més curts de temps i miniaturitzant la instrumentació, permetent així, traslladar gradualment les proves de diagnòstic fora de laboratori central. No obstant això i malgrat els avantatges inherents de l'espectroscòpia de fluorescència resolta en el temps aplicada a la diagnosi molecular, no ha estat fins a l'última dècada que s'han començat a desenvolupar dispositius POC (Point Of Care) basats en la detecció de la fluorescència, degut al desafiament que suposa el desenvolupament de sensors espectroscòpics d'alt rendiment, portàtils i de baix cost. Aquesta tesi presenta el desenvolupament d'un sistema compacte, robust i de baix cost per al diagnòstic molecular basat en l'espectroscòpia de fluorescència resolta en el temps, que serveixi com a plataforma d'ús general per a la detecció òptica d'una varietat de biomarcadors, tancant la bretxa entre el laboratori i el POC dels bioassaigs basats en l'anàlisi de la pèrdua de la fluorescència. En particular, s'han desenvolupat dos sistemes amb diferents nivells d'integració que combinen una matriu unidimensional de píxels SPAD (Single-Photon Avalanch Diode) capaços de detectar un sol fotó, amb un cartutx microfluídic intercanviable emprat per inserir la mostra, així com un díode làser UV premut de baix cost com a font d'excitació. El disseny orientat a la detecció per contacte de l'binomi format pel sensor i la microfluídica, juntament amb l'operació temporitzada dels sensors, permet prescindir de l'ús de lents i filtres. Al seu torn, l'empaquetat a mida de l'xip sensor permet posicionar el cartutx microfluídic directament sobre la matriu de sensors sense cap procediment d'alineament. Tots dos sistemes han estat validats determinant el temps de descomposició de "quantum dots" en 20 nl de solució per a diferents concentracions, emulant així un assaig molecular en un dispositiu POC

    Exploration of Ring Oscillator Based Temperature Sensors Network Accuracy on FPGA

    Get PDF
    During the last decades, technology scaling in reconfigurable logic devices enabled implementing complicated designs which results in higher power density and on-chip temperature. Since higher operating temperature of chips is a critical problem in electronics devices, thermal management techniques are highly required. To provide a thermal map of reconfigurable logic devices, a network of sensors is needed. In this work, a ring-oscillator-based temperature sensor is used to create a sensor network. Then, a design space exploration is done among several sensor networks with the various sensor configurations including different ring oscillator length, the number of sensors in the examined network and various sampling time. We propose three criteria for exploring and comparing the efficiency of sensors network based on the thermal overhead and also measurement accuracy and precision among plenty of configurations on the Virtex-6 FPGA

    Remote Attacks on FPGA Hardware

    Get PDF
    Immer mehr Computersysteme sind weltweit miteinander verbunden und über das Internet zugänglich, was auch die Sicherheitsanforderungen an diese erhöht. Eine neuere Technologie, die zunehmend als Rechenbeschleuniger sowohl für eingebettete Systeme als auch in der Cloud verwendet wird, sind Field-Programmable Gate Arrays (FPGAs). Sie sind sehr flexible Mikrochips, die per Software konfiguriert und programmiert werden können, um beliebige digitale Schaltungen zu implementieren. Wie auch andere integrierte Schaltkreise basieren FPGAs auf modernen Halbleitertechnologien, die von Fertigungstoleranzen und verschiedenen Laufzeitschwankungen betroffen sind. Es ist bereits bekannt, dass diese Variationen die Zuverlässigkeit eines Systems beeinflussen, aber ihre Auswirkungen auf die Sicherheit wurden nicht umfassend untersucht. Diese Doktorarbeit befasst sich mit einem Querschnitt dieser Themen: Sicherheitsprobleme die dadurch entstehen wenn FPGAs von mehreren Benutzern benutzt werden, oder über das Internet zugänglich sind, in Kombination mit physikalischen Schwankungen in modernen Halbleitertechnologien. Der erste Beitrag in dieser Arbeit identifiziert transiente Spannungsschwankungen als eine der stärksten Auswirkungen auf die FPGA-Leistung und analysiert experimentell wie sich verschiedene Arbeitslasten des FPGAs darauf auswirken. In der restlichen Arbeit werden dann die Auswirkungen dieser Spannungsschwankungen auf die Sicherheit untersucht. Die Arbeit zeigt, dass verschiedene Angriffe möglich sind, von denen früher angenommen wurde, dass sie physischen Zugriff auf den Chip und die Verwendung spezieller und teurer Test- und Messgeräte erfordern. Dies zeigt, dass bekannte Isolationsmaßnahmen innerhalb FPGAs von böswilligen Benutzern umgangen werden können, um andere Benutzer im selben FPGA oder sogar das gesamte System anzugreifen. Unter Verwendung von Schaltkreisen zur Beeinflussung der Spannung innerhalb eines FPGAs zeigt diese Arbeit aktive Angriffe, die Fehler (Faults) in anderen Teilen des Systems verursachen können. Auf diese Weise sind Denial-of-Service Angriffe möglich, als auch Fault-Angriffe um geheime Schlüsselinformationen aus dem System zu extrahieren. Darüber hinaus werden passive Angriffe gezeigt, die indirekt die Spannungsschwankungen auf dem Chip messen. Diese Messungen reichen aus, um geheime Schlüsselinformationen durch Power Analysis Seitenkanalangriffe zu extrahieren. In einer weiteren Eskalationsstufe können sich diese Angriffe auch auf andere Chips auswirken die an dasselbe Netzteil angeschlossen sind wie der FPGA. Um zu beweisen, dass vergleichbare Angriffe nicht nur innerhalb FPGAs möglich sind, wird gezeigt, dass auch kleine IoT-Geräte anfällig für Angriffe sind welche die gemeinsame Spannungsversorgung innerhalb eines Chips ausnutzen. Insgesamt zeigt diese Arbeit, dass grundlegende physikalische Variationen in integrierten Schaltkreisen die Sicherheit eines gesamten Systems untergraben können, selbst wenn der Angreifer keinen direkten Zugriff auf das Gerät hat. Für FPGAs in ihrer aktuellen Form müssen diese Probleme zuerst gelöst werden, bevor man sie mit mehreren Benutzern oder mit Zugriff von Drittanbietern sicher verwenden kann. In Veröffentlichungen die nicht Teil dieser Arbeit sind wurden bereits einige erste Gegenmaßnahmen untersucht
    corecore