593 research outputs found

    SCALING UP TASK EXECUTION ON RESOURCE-CONSTRAINED SYSTEMS

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    The ubiquity of executing machine learning tasks on embedded systems with constrained resources has made efficient execution of neural networks on these systems under the CPU, memory, and energy constraints increasingly important. Different from high-end computing systems where resources are abundant and reliable, resource-constrained systems only have limited computational capability, limited memory, and limited energy supply. This dissertation focuses on how to take full advantage of the limited resources of these systems in order to improve task execution efficiency from different aspects of the execution pipeline. While the existing literature primarily aims at solving the problem by shrinking the model size according to the resource constraints, this dissertation aims to improve the execution efficiency for a given set of tasks from the following two aspects. Firstly, we propose SmartON, which is the first batteryless active event detection system that considers both the event arrival pattern as well as the harvested energy to determine when the system should wake up and what the duty cycle should be. Secondly, we propose Antler, which exploits the affinity between all pairs of tasks in a multitask inference system to construct a compact graph representation of the task set for a given overall size budget. To achieve the aforementioned algorithmic proposals, we propose the following hardware solutions. One is a controllable capacitor array that can expand the system’s energy storage on-the-fly. The other is a FRAM array that can accommodate multiple neural networks running on one system.Doctor of Philosoph

    The Impact of Lithium Ion on the Application of Resistive Switching Devices

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    With the development of the times, people have higher and higher requirements for storage equipment. Many new storage devices have emerged, such as Magnetoresistive random-access memory(MRAM) and Resistive random-access memory(ReRAM). The junction structure is the basic unit of these two storage devices, and in this paper, the MTJ and resistive switching junction are tuned with lithium fluoride(LiF) to optimize their performance, respectively. In the first experiment, a magnetic tunnelling junction resembling a battery is developed and proved to be electromagnetically tuneable. In this LiF-based device, reversible non-volatile resistive switching phenomena and tunnelling phenomena coexist, enabling four well-defined groups for each device. The management of the interface enables the spin transfer of actively controlled devices, hence enhancing their application potential. In the second experiment, 796 devices were measured. For the resistive switching device with TiO as the insulating layer, adding additional LiF layer can significantly increase the probability of resistive switching phenomenon, and adding an appropriate thickness of LiF can also increase the differentiation between high and low group states, which is beneficial for the regulation of resistive switching devices

    Wearable sensors for respiration monitoring: a review

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    This paper provides an overview of flexible and wearable respiration sensors with emphasis on their significance in healthcare applications. The paper classifies these sensors based on their operating frequency distinguishing between high-frequency sensors, which operate above 10 MHz, and low-frequency sensors, which operate below this level. The operating principles of breathing sensors as well as the materials and fabrication techniques employed in their design are addressed. The existing research highlights the need for robust and flexible materials to enable the development of reliable and comfortable sensors. Finally, the paper presents potential research directions and proposes research challenges in the field of flexible and wearable respiration sensors. By identifying emerging trends and gaps in knowledge, this review can encourage further advancements and innovation in the rapidly evolving domain of flexible and wearable sensors.This work was supported by the Spanish Government (MICINN) under Projects TED2021-131209B-I00 and PID2021-124288OB-I00.Peer ReviewedPostprint (published version

    Approximate Computing Survey, Part II: Application-Specific & Architectural Approximation Techniques and Applications

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    The challenging deployment of compute-intensive applications from domains such Artificial Intelligence (AI) and Digital Signal Processing (DSP), forces the community of computing systems to explore new design approaches. Approximate Computing appears as an emerging solution, allowing to tune the quality of results in the design of a system in order to improve the energy efficiency and/or performance. This radical paradigm shift has attracted interest from both academia and industry, resulting in significant research on approximation techniques and methodologies at different design layers (from system down to integrated circuits). Motivated by the wide appeal of Approximate Computing over the last 10 years, we conduct a two-part survey to cover key aspects (e.g., terminology and applications) and review the state-of-the art approximation techniques from all layers of the traditional computing stack. In Part II of our survey, we classify and present the technical details of application-specific and architectural approximation techniques, which both target the design of resource-efficient processors/accelerators & systems. Moreover, we present a detailed analysis of the application spectrum of Approximate Computing and discuss open challenges and future directions.Comment: Under Review at ACM Computing Survey

    Toward Fault-Tolerant Applications on Reconfigurable Systems-on-Chip

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    L'abstract è presente nell'allegato / the abstract is in the attachmen

    Pressure sensing of silicon nanowires

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    Nanostructures made from crystalline silicon, especially in the form of nanowires (SiNWs), have shown great potential as pressure sensors due to their unique properties such as high sensitivity, small size, and low power consumption. When a force is applied to SiNWs, they undergo a mechanical deformation that results in a change in their electrical resistance. Such an effect has been referred to as the piezoresistance effect. This change in resistance can be measured and used to determine the amount of pressure being applied. By integrating these nanowires into a sensor device, it is possible to create a highly sensitive pressure sensor that can be used in a variety of applications such as in medical devices, aerospace technology, and robotics. Many available techniques can be applied to fabricate such SiNWs. One of the simplest ones is the so-called metal-assisted-chemical-etching (MACE) which has gained significant attention in recent years. This process involves the use of a metal catalyst, such as silver, to etch silicon in a controlled manner to produce nanowires with high aspect ratios. The nanowires can be integrated with other materials to create a flexible and stretchable sensor that can conform to curved surfaces and be used in a variety of applications. One advantage of using MACE to fabricate silicon nanowires is that it is a low-cost and scalable process. This makes it possible to produce large quantities of nanowires at a low cost, which is important for commercial applications. This thesis describes the fabrication of SiNWs using MACE and applications of the SiNWs as an accurate and sensitive pressure sensor for an isostatic and uniaxial load. Its use was further extended to fabricate a novel, small, and compact, breath sensor that could potentially have an impact on sleep research

    Low Power Memory/Memristor Devices and Systems

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    This reprint focusses on achieving low-power computation using memristive devices. The topic was designed as a convenient reference point: it contains a mix of techniques starting from the fundamental manufacturing of memristive devices all the way to applications such as physically unclonable functions, and also covers perspectives on, e.g., in-memory computing, which is inextricably linked with emerging memory devices such as memristors. Finally, the reprint contains a few articles representing how other communities (from typical CMOS design to photonics) are fighting on their own fronts in the quest towards low-power computation, as a comparison with the memristor literature. We hope that readers will enjoy discovering the articles within

    Medical devices with embedded electronics: design and development methodology for start-ups

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    358 p.El sector de la biotecnología demanda innovación constante para hacer frente a los retos del sector sanitario. Hechos como la reciente pandemia COVID-19, el envejecimiento de la población, el aumento de las tasas de dependencia o la necesidad de promover la asistencia sanitaria personalizada tanto en entorno hospitalario como domiciliario, ponen de manifiesto la necesidad de desarrollar dispositivos médicos de monitorización y diagnostico cada vez más sofisticados, fiables y conectados de forma rápida y eficaz. En este escenario, los sistemas embebidos se han convertido en tecnología clave para el diseño de soluciones innovadoras de bajo coste y de forma rápida. Conscientes de la oportunidad que existe en el sector, cada vez son más las denominadas "biotech start-ups" las que se embarcan en el negocio de los dispositivos médicos. Pese a tener grandes ideas y soluciones técnicas, muchas terminan fracasando por desconocimiento del sector sanitario y de los requisitos regulatorios que se deben cumplir. La gran cantidad de requisitos técnicos y regulatorios hace que sea necesario disponer de una metodología procedimental para ejecutar dichos desarrollos. Por ello, esta tesis define y valida una metodología para el diseño y desarrollo de dispositivos médicos embebidos
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