541 research outputs found

    Energy-driven computing for energy-harvesting embedded systems

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    There has been increasing interest over the last decade in the powering of embedded systems from ‘harvested’ energy, and this has been further fuelled by the promise and vision of IoT. Energy harvesting systems present numerous challenges, although some of these are also posed by their battery-powered counterparts: e.g. ultra-low power consumption. However, a significant challenge not witnessed in battery-powered systems is a requirement to manage the combination of a highly unpredictable and variable (spatially and temporally) power supply with a highly dynamic (across many orders of magnitude) and often event-driven system power consumption. This problem is typically rectified through the addition of energy storage (e.g. a supercapacitor) to provide energy buffering to smooth out the dynamics of supply and consumption. This has the significant advantage of making the system ‘look like’ a battery-powered system, yet usually adds volume, mass and cost to the resultant system – something that is counterproductive in future flexible, wearable and implantable IoT systems. Such systems can, alternatively, include only a very small amount (or even zero) energy-storage. Now, instead of the system’s operation being dictated solely by the application, operation starts to become ‘energy-driven’, with execution being highly intertwined with power and energy availability. In this presentation, I will first introduce the landscape of energy-harvesting computing systems, and articulate how energy-driven computing presents a different class of computing to conventional approaches. A significant issue in the successful operation of these systems is their ability to operate from an intermittent, constrained and variable supply, and I will show how transient operation and power-neutrality can be used to achieve the vision for these systems, and hence enable the proliferation of tiny self-powered systems that will underpin much of the IoT

    Energy-Driven Computing: Rethinking the Design of Energy Harvesting Systems

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    Energy harvesting computing has been gaining increasing traction over the past decade, fueled by technological developments and rising demand for autonomous and battery-free systems. Energy harvesting introduces numerous challenges to embedded systems but, arguably the greatest, is the required transition from an energy source that typically provides virtually unlimited power for a reasonable period of time until it becomes exhausted, to a power source that is highly unpredictable and dynamic (both spatially and temporally, and with a range spanning many orders of magnitude). The typical approach to overcome this is the addition of intermediate energy storage/buffering to smooth out the temporal dynamics of both power supply and consumption. This has the advantage that, if correctly sized, the system ‘looks like’ a battery-powered system; however, it also adds volume, mass, cost and complexity and, if not sized correctly, unreliability. In this paper, we consider energy-driven computing, where systems are designed from the outset to operate from an energy harvesting source. Such systems typically contain little or no additional energy storage (instead relying on tiny parasitic and decoupling capacitance), alleviating the aforementioned issues. Examples of energy-driven computing include transient systems (which power down when the supply disappears and efficiently continue execution when it returns) and power-neutral systems (which operate directly from the instantaneous power harvested, gracefully modulating their consumption and performance to match the supply). In this paper, we introduce a taxonomy of energy-driven computing, articulating how power-neutral, transient, and energy-driven systems present a different class of computing to conventional approaches

    Computational Sprinting

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    Although transistor density continues to increase, voltage scaling has stalled and thus power density is increasing each technology generation. Particularly in mobile devices, which have limited cooling options, these trends lead to a utilization wall in which sustained chip performance is limited primarily by power rather than area. However, many mobile applications do not demand sustained performance; rather they comprise short bursts of computation in response to sporadic user activity. To improve responsiveness for such applications, this paper explores activating otherwise powered-down cores for sub-second bursts of intense parallel computation. The approach exploits the concept of computational sprinting, in which a chip temporarily exceeds its sustainable thermal power budget to provide instantaneous throughput, after which the chip must return to nominal operation to cool down. To demonstrate the feasibility of this approach, we analyze the thermal and electrical characteristics of a smart-phone-like system that nominally operates a single core (~1W peak), but can sprint with up to 16 cores for hundreds of milliseconds. We describe a thermal design that incorporates phase-change materials to provide thermal capacitance to enable such sprints. We analyze image recognition kernels to show that parallel sprinting has the potential to achieve the task response time of a 16W chip within the thermal constraints of a 1W mobile platform

    Energy-Efficient System Architectures for Intermittently-Powered IoT Devices

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    Various industry forecasts project that, by 2020, there will be around 50 billion devices connected to the Internet of Things (IoT), helping to engineer new solutions to societal-scale problems such as healthcare, energy conservation, transportation, etc. Most of these devices will be wireless due to the expense, inconvenience, or in some cases, the sheer infeasibility of wiring them. With no cord for power and limited space for a battery, powering these devices for operating in a set-and-forget mode (i.e., achieve several months to possibly years of unattended operation) becomes a daunting challenge. Environmental energy harvesting (where the system powers itself using energy that it scavenges from its operating environment) has been shown to be a promising and viable option for powering these IoT devices. However, ambient energy sources (such as vibration, wind, RF signals) are often minuscule, unreliable, and intermittent in nature, which can lead to frequent intervals of power loss. Performing computations reliably in the face of such power supply interruptions is challenging

    Normal Galaxies in the Infrared

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    This review addresses what can be learned from infrared observations about galaxies powered predominantly by star formation. Infrared techniques mostly probe the interstellar medium of galaxies, yielding physical and chemical information on the medium out of which stars form, which is in turn affected by those stars. Methods traditionally used in the study of such normal galaxies at wavelengths longer than 3 microns are described, and major questions currently pursued in the field are outlined. The most prominent results from the IRAS survey are reviewed. Contributions by ISO in the field of broad-band photometry are then presented, followed by ISO results in spectrospcopy. Normal galaxy studies not directly concerned with the ISM are quickly reviewed. The outlook and challenges in pursuing the interpretation of infrared data on the ISM are discussed.Comment: 39 pages including 10 figures; Lecture notes from the Les Houches Summer School "Infrared Astronomy: Today and Tomorrow," August 1998. Editors F. Casoli and J. Lequeu

    Long-term light-curves of transient X-ray pulsars as a tool to study disk−magnetosphere interaction

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    X-ray pulsars are highly magnetized neutron stars in close binary systems accreting matter from a normal companion star. Their strong magnetic fields channel the accreting matter onto the magnetic poles of the neutron star, releasing an enormous amount of energy in the form of X-rays. If the matter accreted from the stellar companion carries a large amount of angular momentum, it will generally form an accretion disk around the neutron star. The strong magnetic field of the neutron star will effectively truncate the accretion disk at the magnetospheric radius, which defines the space around the X-ray pulsar known as the magnetosphere. The observed behavior of the X-ray pulsar will depend on several factors, including the rate at which mass is being accreted. At low mass accretion rates, the magnetosphere will be able to extend further out, and if the mass accretion rate drops below some critical value, accreting matter will be stopped by the centrifugal barrier created by the neutron star’s rapidly spinning magnetosphere. This is known as the propeller effect, because matter is basically flung out by the rapidly spinning magnetosphere. The propeller effect is generally used to explain the declining phases of the outbursts of transient X-ray pulsars into quiescence. Another phenomenon recently proposed for transient X-ray pulsars is the possibility of accretion from a cold accretion disk at low mass accretion rates. This is caused by a thermal-viscous instability developing in the accretion disk and is commonly invoked to explain the characteristic outbursts of dwarf novae, where the theory is encapsulated in the disk instability model (DIM). During the decay of dwarf nova outbursts, a propagating cooling front will appear in the accretion disk, and when this front reaches the inner disk radius, the entire disk will be in a cold state of neutral hydrogen. In this thesis, the data from observations made by the Swift observatory have been analyzed with the intent to test the possibility of the appearance of a propagating cooling front during the decaying phases of the outbursts of transient X-ray pulsars. The light-curves of three sources (SMC X-2, 4U 0115+63, V 0332+53) were fitted with a smoothed spline representing the observed behavior, which was subsequently compared to the modeled luminosity decay caused by the propagation of a cooling front. The result of the analysis is that by modeling the expected behavior of a cooling front propagating through the accretion disk, the luminosity decay of these transient X-ray pulsar’s outbursts can be well explained without the need to invoke the propeller effect. Additionally, the obtained αcold values are consistent with the values commonly used in the DIM

    Energy-aware design of hardware and software for ultra-low-power systems

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    Future visions of the Internet of Things and Industry 4.0 demand for large scale deployments of mobile devices while removing the numerous disadvantages of using batteries: degradation, scale, weight, pollution, and costs. However, this requires computing platforms with extremely low energy consumptions, and thus employ ultra-low-power hardware, energy harvesting solutions, and highly efficient power-management hardware and software. The goal of these power management solutions is to either achieve power neutrality, a condition where energy harvest and energy consumption equalize while maximizing the service quality, or to enhance power efficiency for conserving energy reserves. To reach these goals, intelligent power-management decisions are needed that utilize precise energy data. This thesis discusses the measurement of energy in embedded systems, both online and by external equipment, and the utilization of the acquired data for modeling the power consumption states of each involved hardware component. Furthermore, a method is shown to use the resulting models by instrumenting preexisting device drivers. These drivers enable new functionalities, such as online energy accounting and energy application interfaces, and facilitate intelligent power management decisions. In order to reduce additional efforts for device driver reimplementation and the violation of the separation of concerns paradigm, the approach shown in this thesis synthesizes instrumentation aspects for an aspect oriented programming language, so that the original device-driver source code remains unaffected. Eventually, an automated process of energy measurement and data analysis is presented. This process is able to yield precise energy models with low manual effort. In combination with the instrumentation synthesis of aspect code, this method enables an accelerated creation process for energy models of ultra-low-power systems. For all proposed methods, empirical accuracy and overhead measurements are presented. To support the claims of the author, first practical energy aware and wireless-radio networked applications are showcased: An energy-neutral light sensor, a photovoltaic-powered seminar-room door plate, and a sensor network experiment testbed for research and education

    Quiet Clean Short-haul Experimental Engine (QCSEE) over-the-wing engine and control simulation results

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    A hybrid-computer simulation of the over the wing turbofan engine was constructed to develop the dynamic design of the control. This engine and control system includes a full authority digital electronic control using compressor stator reset to achieve fast thrust response and a modified Kalman filter to correct for sensor failures. Fast thrust response for powered-lift operations and accurate, fast responding, steady state control of the engine is provided. Simulation results for throttle bursts from 62 to 100 percent takeoff thrust predict that the engine will accelerate from 62 to 95 percent takeoff thrust in one second
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