1,721,237 research outputs found
Run-time power and energy management of many-core systems
No full textEnergy-efficiency is important at all scales of computing system, from microcontrollers through to HPC. Established mechanisms like DPM and DVFS provide controls to affect power consumption, but careful management is required for effective use. In this talk, I provide an overview of the different run-time power management (RTM) approaches that we have developed, explored and practically validated through the EPSRC PRiME programme grant (www.prime-project.org), and discuss key findings and lessons learnt. I also refer to a range of open-source tools that we have released as a result of the project, from multi-core power modelling to a cross-platform framework for RTM
Managing power in heterogeneous multicore systems
No full textPower- and energy-efficiency continues to be a primary concern in the design and management of computing systems, through from mobile devices (battery life and temperature) to HPC (electricity bills and temperature). Managing this is an increasingly complex task, as systems shift from having a single processing element to multi- and many-core computing platforms with numerous cores of differing types. In this talk I will present our research into the runtime management (RTM) of such systems that have come out of the PRiME (www.prime-project.org) research project. I will present a range of different approaches that we have developed and experimentally validated, and the key findings that we have made along the way. These encompass 1) exploring RTM on both novel and heterogeneous/homogeneous COTS multi-core platforms, 2) the impact of core scaling on RTMs, 3) issues and approaches for managing concurrently executing workloads on shared resource, and 4) comparing the impact of offline vs online characterisation approaches. I will also present a range of open-source tools that we have developed and released through these projects, spanning simulation and runtime power models for multi-core CPUs, to a framework for researchers to incorporate multi-core runtime management into their system and enable level comparison with the SoA
Run-time power management of multi- and many-core systems
No full textPower- and energy-efficiency continues to be a primary concern in the design and management of computing systems, through from mobile devices (battery life and temperature) to HPC (electricity bills and temperature). In this talk I will give a summary of our research into the runtime management (RTM) of multi- and many-core computing systems, that have come out of the PRiME (www.prime-project.org) and Graceful research projects. I will present a range of different approaches that we have developed and experimentally validated, and the key findings that we have made along the way. These encompass 1) exploring RTM on both novel and heterogeneous/homogeneous COTS multi-core platforms, 2) the impact of core scaling on RTMs, 3) issues and approaches for managing concurrently executing workloads on shared resource, and 4) comparing the impact of offline vs online characterisation approaches. I will also present a range of open-source tools that we have developed and released through these projects, spanning simulation and runtime power models for multi-core CPUs, to a framework for researchers to incorporate multi-core runtime management into their system and enable level comparison with the SoA
Energy-driven systems and compute: Towards self-powered embedded computing systems
No full textAn energy harvester is a small part of a larger embedded system. Historically, such systems have typically been designed in the same way as their battery-powered systems, often adding significant complexity to make the harvester 'appear' to the load as if it were in fact a battery. In this talk, I will propose an alternative approach, that of energy-driven computing, where the design of applications and systems is rethought such that the energy environment is a key factor in the design process. I will illustrate this through two approaches: intermittent computing and power-neutral computing, highlighting the challenges and opportunities that they bring
Energy-driven computing for energy-harvesting embedded systems
No full textThere 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
Efficient deployment of UAV-powered sensors for optimal coverage and connectivity
No full textThe Internet of Things (IoT) digitizes the physical world with wireless devices sensing their surroundings and delivering periodic notifications of parameters they are monitoring. However, this operation is bound by finite-capacity batteries, in which replenishment is practically infeasible due to the envisioned size of the IoT networks. By also considering the autonomous and self-sufficient service vision of the IoT paradigm, the need for novel approaches overcoming the energy constraints is evident. Here, unmanned aerial vehicles (UAVs) come into prominence. The UAVs can remotely energize wireless devices, via wireless power transfer (WPT), and thus guarantee reliable sensing coverage as well as longevity in the IoT domain. However, this can be only achieved by the precise alignment of both UAVs and wireless devices. Thus, this paper presents an efficient deployment strategy based on the circle packing problem, in which a lower-bound for the required number of wireless devices achieving optimal coverage is derived. The analysis, based on empirical measurements, reveals the design considerations for an energy harvesting (EH)-aided UAV scenario with regard to Federal Communications Commission (FCC) regulations, power consumption of wireless devices, and reporting frequency requirements of the IoT applications. Our results elaborate on a number of trade-offs, based on UAV, device, and medium characteristics, and provide realistic guidelines, achieving optimal coverage while meeting application requirements
Energy harvesting and transient computing: a paradigm shift for embedded systems?
No full textEmbedded systems powered from time-varying energy harvesting sources traditionally operate using the principles of energy-neutral computing: over a certain period of time, the energy that they consume equals the energy that they harvest. This has the significant advantage of making the system ‘look like’ a battery-powered system, yet typically results in large, complex and expensive power conversion circuitry and introduces numerous challenges including fast and reliable cold-start. In recent years, the concept of transient computing has emerged to challenge this traditional approach, whereby low-power embedded systems are enabled to operate as usual while energy is available but, after loss of supply, can quickly regain state and continue where they left off. This paper provides a summary of these different approaches
Transient and power-neutral computing: a paradigm shift for embedded systems?
No full textEmbedded systems powered from time-varying energy harvesting power sources, for example solar PV or mechanical vibration, have traditionally operated using the principles of energy-neutral computing. That is, over a sensible period of time (e.g. 24 hours), the energy consumed is equal to the energy that was harvested. This has the advantage of making the system ‘look like’ a battery-powered system, yet typically results in large, complex and expensive power conversion circuitry and introduces challenges such as fast and reliable cold-start. In recent years, the concept of transient computing has emerged to challenge this, whereby low-power embedded systems can be designed to operate and perform useful computation when energy is available, and carefully ‘hibernate’ when the power disappears such that it can continue where it left off when supply is regained. In this talk I will explain this shift towards transient computing and the different approaches that have been proposed, and the new challenges that are raised as a result. I will also discuss a complementary approach to the powering of transient systems, named power-neutral computing. Instead of equating energy consumption to energy supply, as is the case in energy-neutral systems, power-neutral systems attempt to match instantaneous power consumption to the instantaneous power supplied. This fine-grained control permits better use of available resources while overcoming the disadvantages of energy-neutral computing; furthermore, it can work alongside aforementioned transient computing techniques if supply disappears altogether
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