124 research outputs found

    On microelectronic self-learning cognitive chip systems

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    After a brief review of machine learning techniques and applications, this Ph.D. thesis examines several approaches for implementing machine learning architectures and algorithms into hardware within our laboratory. From this interdisciplinary background support, we have motivations for novel approaches that we intend to follow as an objective of innovative hardware implementations of dynamically self-reconfigurable logic for enhanced self-adaptive, self-(re)organizing and eventually self-assembling machine learning systems, while developing this new particular area of research. And after reviewing some relevant background of robotic control methods followed by most recent advanced cognitive controllers, this Ph.D. thesis suggests that amongst many well-known ways of designing operational technologies, the design methodologies of those leading-edge high-tech devices such as cognitive chips that may well lead to intelligent machines exhibiting conscious phenomena should crucially be restricted to extremely well defined constraints. Roboticists also need those as specifications to help decide upfront on otherwise infinitely free hardware/software design details. In addition and most importantly, we propose these specifications as methodological guidelines tightly related to ethics and the nowadays well-identified workings of the human body and of its psyche

    Distribution of Low Latency Machine Learning Algorithm

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    Mobile networks are evolving towards centralization and cloudification while bringing computing power to the edge, opening its scope to a new range of applications. Ultra-low latency is one of the requirements of such applications in the next generation of mobile networks (5G), where deep learning is expected to play a big role. Hence, to enable the usage of deep learning solutions on the edge cloud, ultra-low latency inference must be investigated. The study presented here relies on the usage of an in-house framework (CRUN) that enables the distribution of acceleration on data center environment. The objective of this thesis is to leverage the best solution for the inference of a machine learning algorithm for an anomaly detection application using neural networks in the edge cloud context. To evaluate the obtained results with CRUN a comparison work is also carried out. Five inference solutions were compared using CPU, GPU and FPGA. The results show a superior performance in terms of latency for all CRUN experiments, that basically comprehends three cases. The first one utilizing the RTL anomaly detection neural network as a baseline solution, the second using the same baseline code but unrolling the biggest layer for obtaining reduced latency and the third by distributing the neural network in two FPGAs. The requirements for this solution were to obtain latency between 20 μs to 40 μs for inference time and at least 20000 inferences per second. These goals were categorically fulfilled for all CRUN experiments, providing 30 μs latency in average, while the second best solution provided 272 μs

    Security and Privacy of Radio Frequency Identification

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    Tanenbaum, A.S. [Promotor]Crispo, B. [Copromotor

    Real-time Neuromorphic Visual Pre-Processing and Dynamic Saliency

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    The human brain is by far the most computationally complex, efficient, and reliable computing system operating under such low-power, small-size, and light-weight specifications. Within the field of neuromorphic engineering, we seek to design systems with facsimiles to that of the human brain with means to reach its desirable properties. In this doctoral work, the focus is within the realm of vision, specifically visual saliency and related visual tasks with bio-inspired, real-time processing. The human visual system, from the retina through the visual cortical hierarchy, is responsible for extracting visual information and processing this information, forming our visual perception. This visual information is transmitted through these various layers of the visual system via spikes (or action potentials), representing information in the temporal domain. The objective is to exploit this neurological communication protocol and functionality within the systems we design. This approach is essential for the advancement of autonomous, mobile agents (i.e. drones/MAVs, cars) which must perform visual tasks under size and power constraints in which traditional CPU or GPU implementations to not suffice. Although the high-level objective is to design a complete visual processor with direct physical and functional correlates to the human visual system, we focus on three specific tasks. The first focus of this thesis is the integration of motion into a biologically-plausible proto-object-based visual saliency model. Laurent Itti, one of the pioneers in the field, defines visual saliency as ``the distinct subjective perceptual quality which makes some items in the world stand out from their neighbors and immediately grab our attention.'' From humans to insects, visual saliency is important for the extraction of only interesting regions of visual stimuli for further processing. Prior to this doctoral work, Russel et al. \cite{russell2014model} designed a model of proto-object-based visual saliency with biological correlates. This model was designed for computing saliency only on static images. However, motion is a naturally occurring phenomena that plays an essential role in both human and animal visual processing. Henceforth, the most ideal model of visual saliency should consider motion that may be exhibited within the visual scene. In this work a novel dynamic proto-object-based visual saliency is described which extends the Russel et. al. saliency model to consider not only static, but also temporal information. This model was validated by using metrics for determining how accurate the model is in predicting human eye fixations and saccades on a public dataset of videos with attached eye tracking data. This model outperformed other state-of-the-art visual saliency models in computing dynamic visual saliency. Such a model that can accurately predict where humans look, can serve as a front-end component to other visual processors performing tasks such as object detection and recognition, or object tracking. In doing so it can reduce throughput and increase processing speed for such tasks. Furthermore, it has more obvious applications in artificial intelligence in mimicking the functionality of the human visual system. The second focus of this thesis is the implementation of this visual saliency model on an FPGA (Field Programmable Gate Array) for real-time processing. Initially, this model was designed within MATLAB, a software-based approach running on a CPU, which limits the processing speed and consumes unnecessary amounts of power due to overhead. This is detrimental for integration with an autonomous, mobile system which must operate in real-time. This novel FPGA implementation allows for a low-power, high-speed approach to computing visual saliency. There are a few existing FPGA-based implementations of visual saliency, and of those, none are based on the notion of proto-objects. This work presents the first, to our knowledge, FPGA implementation of an object-based visual saliency model. Such an FPGA implementation allows for the low-power, light-weight, and small-size specifications that we seek within the field of neuromorphic engineering. For validating the FPGA model, the same metrics are used for determining the extent to which it predicts human eye saccades and fixations. We compare this hardware implementation to the software model for validation. The third focus of this thesis is the design of a generic neuromorphic platform both on FPGA and VLSI (Very-Large-Scale-Integration) technology for performing visual tasks, including those necessary in the computation of the visual saliency. Visual processing tasks such as image filtering and image dewarping are demonstrated via this novel neuromorphic technology consisting of an array of hardware-based generalized integrate-and-fire neurons. It allows the visual saliency model's computation to be offloaded onto this hardware-based architecture. We first demonstrate an emulation of this neuromorphic system on FPGA demonstrating its capability of dewarping and filtering tasks as well as integration with a neuromorphic camera called the ATIS (Asynchronous Time-based Image Sensor). We then demonstrate the neuromorphic platform implemented in CMOS technology, specifically designed for low-mismatch, high-density, and low-power. Such a VLSI technology-based platform further bridges the gap between engineering and biology and moves us closer towards developing a complete neuromorphic visual processor

    Neuromorphic auditory computing: towards a digital, event-based implementation of the hearing sense for robotics

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    In this work, it is intended to advance on the development of the neuromorphic audio processing systems in robots through the implementation of an open-source neuromorphic cochlea, event-based models of primary auditory nuclei, and their potential use for real-time robotics applications. First, the main gaps when working with neuromorphic cochleae were identified. Among them, the accessibility and usability of such sensors can be considered as a critical aspect. Silicon cochleae could not be as flexible as desired for some applications. However, FPGA-based sensors can be considered as an alternative for fast prototyping and proof-of-concept applications. Therefore, a software tool was implemented for generating open-source, user-configurable Neuromorphic Auditory Sensor models that can be deployed in any FPGA, removing the aforementioned barriers for the neuromorphic research community. Next, the biological principles of the animals' auditory system were studied with the aim of continuing the development of the Neuromorphic Auditory Sensor. More specifically, the principles of binaural hearing were deeply studied for implementing event-based models to perform real-time sound source localization tasks. Two different approaches were followed to extract inter-aural time differences from event-based auditory signals. On the one hand, a digital, event-based design of the Jeffress model was implemented. On the other hand, a novel digital implementation of the Time Difference Encoder model was designed and implemented on FPGA. Finally, three different robotic platforms were used for evaluating the performance of the proposed real-time neuromorphic audio processing architectures. An audio-guided central pattern generator was used to control a hexapod robot in real-time using spiking neural networks on SpiNNaker. Then, a sensory integration application was implemented combining sound source localization and obstacle avoidance for autonomous robots navigation. Lastly, the Neuromorphic Auditory Sensor was integrated within the iCub robotic platform, being the first time that an event-based cochlea is used in a humanoid robot. Then, the conclusions obtained are presented and new features and improvements are proposed for future works.En este trabajo se pretende avanzar en el desarrollo de los sistemas de procesamiento de audio neuromórficos en robots a través de la implementación de una cóclea neuromórfica de código abierto, modelos basados en eventos de los núcleos auditivos primarios, y su potencial uso para aplicaciones de robótica en tiempo real. En primer lugar, se identificaron los principales problemas a la hora de trabajar con cócleas neuromórficas. Entre ellos, la accesibilidad y usabilidad de dichos sensores puede considerarse un aspecto crítico. Los circuitos integrados analógicos que implementan modelos cocleares pueden no pueden ser tan flexibles como se desea para algunas aplicaciones específicas. Sin embargo, los sensores basados en FPGA pueden considerarse una alternativa para el desarrollo rápido y flexible de prototipos y aplicaciones de prueba de concepto. Por lo tanto, en este trabajo se implementó una herramienta de software para generar modelos de sensores auditivos neuromórficos de código abierto y configurables por el usuario, que pueden desplegarse en cualquier FPGA, eliminando las barreras mencionadas para la comunidad de investigación neuromórfica. A continuación, se estudiaron los principios biológicos del sistema auditivo de los animales con el objetivo de continuar con el desarrollo del Sensor Auditivo Neuromórfico (NAS). Más concretamente, se estudiaron en profundidad los principios de la audición binaural con el fin de implementar modelos basados en eventos para realizar tareas de localización de fuentes sonoras en tiempo real. Se siguieron dos enfoques diferentes para extraer las diferencias temporales interaurales de las señales auditivas basadas en eventos. Por un lado, se implementó un diseño digital basado en eventos del modelo Jeffress. Por otro lado, se diseñó una novedosa implementación digital del modelo de codificador de diferencias temporales y se implementó en FPGA. Por último, se utilizaron tres plataformas robóticas diferentes para evaluar el rendimiento de las arquitecturas de procesamiento de audio neuromórfico en tiempo real propuestas. Se utilizó un generador central de patrones guiado por audio para controlar un robot hexápodo en tiempo real utilizando redes neuronales pulsantes en SpiNNaker. A continuación, se implementó una aplicación de integración sensorial que combina la localización de fuentes de sonido y la evitación de obstáculos para la navegación de robots autónomos. Por último, se integró el Sensor Auditivo Neuromórfico dentro de la plataforma robótica iCub, siendo la primera vez que se utiliza una cóclea basada en eventos en un robot humanoide. Por último, en este trabajo se presentan las conclusiones obtenidas y se proponen nuevas funcionalidades y mejoras para futuros trabajos

    Hands on Media History:A New Methodology in the Humanities and Social Sciences

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