Power neutral performance scaling for energy harvesting MP-SoCs

Fletcher, Benjamin J.; Balsamo, Domenico; Merrett, Geoff V.

doi:10.23919/date.2017.7927231

Cited by 10 publications

(12 citation statements)

References 11 publications

(11 reference statements)

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“…Both methods may exploit trade-offs between QoS and power consumption by modulating their operating frequency, activating and deactivating peripherals, selectively sampling sensors, and/or using techniques such as approximate computing [26][27][28][29].…”

Section: Energy-and Power-neutral Computingmentioning

confidence: 99%

“…Modulating the power consumption is typically done through dynamic performance scaling (e.g. dynamic voltage and/or frequency scaling) [13,[27][28][29], although it could potentially also employ other techniques such as approximate computing. However, real computational devices have fixed operating points in terms of power and performance, and switching between them takes time, so real power-neutral devices still have to obey (2.1), albeit over a much smaller period T (typically on the order of milliseconds).…”

Section: )mentioning

confidence: 99%

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Energy-driven computing

Sliper

Cetinkaya

Weddell

et al. 2019

Phil. Trans. R. Soc. A.

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For decades, the design of untethered devices has been focused on delivering a fixed quality of service with minimum power consumption, to enable battery-powered devices with reasonably long deployment lifetime. However, to realize the promised tens of billions of connected devices in the Internet of Things, computers must operate autonomously and harvest ambient energy to avoid the cost and maintenance requirements imposed by mains- or battery-powered operation. But harvested power typically fluctuates, often unpredictably, and with large temporal and spatial variability. Energy-driven computers are designed to treat energy-availability as a first-class citizen, in order to gracefully adapt to the dynamics of energy harvesting. They may sleep through periods of no energy, endure periods of scarce energy, and capitalize on periods of ample energy. In this paper, we describe the promise and limitations of energy-driven computing, with an emphasis on intermittent operation. This article is part of the theme issue ‘Harmonizing energy-autonomous computing and intelligence’.

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Section: Energy-and Power-neutral Computingmentioning

confidence: 99%

Section: )mentioning

confidence: 99%

Energy-driven computing

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Cetinkaya

Weddell

et al. 2019

Phil. Trans. R. Soc. A.

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“…To extend the range of operation and better utilise harvested power, Balsamo et al proposed the concept of power-neutral operation, whereby performance scaling is used to instantaneously match power consumption with harvested power [2]. Fletcher et al extended this concept by implementing power-neutral operation on a heterogeneous multiprocessor system-on-chip (MPSoC) by using a combination of dynamic voltage and frequency scaling (DVFS) and dynamic power management (runtime enabling/disabling of CPU cores) [10]. Power-neutral systems have negligible energy storage, so must react quickly to variations in supply, and can only operate for a very short time if supply drops below minimum.…”

Section: Introductionmentioning

confidence: 99%

“…Even for power-neutral systems, IC is needed to preserve progress through periods of insufficient power. IC was included in the microcontroller approach to power-neutral computing [2], but was not included in the method for MPSoCs [10] -likely because of the complexity and viability of implementing IC on the desktop Linux operating system used in their work and the lack of existing IC methods for high-performance systems.…”

Section: Introductionmentioning

confidence: 99%

Enabling intermittent computing on high-performance out-of-order processors

Sliper

Balsamo

Weddell

et al. 2018

Proceedings of the 6th International Workshop on Energy Harvesting &Amp; Energy-Neutral Sensing Systems

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Intermittent computing is a new paradigm enabling battery-less computing devices to be powered directly from energy harvesting, enabling IoT devices that are free from the cost, size and lifetime constraints of batteries. To cope with frequent power interruptions, intermittent computing systems save computational progress before power is lost, and restore it when power returns. Recent research in power-neutral operation of multiprocessor system-onchips (MPSoCs), where performance scaling is used to instantaneously match power consumption with supply, motivates the need for intermittent computing on high-performance systems. Existing works provide solutions for microcontrollers, but with the increased complexity of high-performance SoCs, new challenges such as hierarchical memory and dependence on large existing libraries emerge. In this paper, we provide a taxonomy of published intermittent computing methods and identify the most suitable method for high-performance SoCs. The chosen method is then implemented and experimentally validated on an Arm A9 out-of-order application processor. Results show that state can be saved/restored correctly in 8.6 ms for a minimal bare-metal application, which is an order of magnitude faster than the platform's hardware boot time. CCS CONCEPTS • Computer systems organization → Embedded systems; Embedded software; System on a chip;

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“…An example of this can be seen in Fig. 6, which plots the power consumption of an ODROID XU-4 platform (containing an ARM eight-core big.LITTLE processor) against performance when executing a raytracing application [11]. The points plotted are different operating points obtained through combinations of core frequency (DVFS) and enabled CPU cores; the power consumption can be modulated by an order of magnitude through this.…”

Section: Power-neutral Computingmentioning

confidence: 99%

Energy-driven computing: Rethinking the design of energy harvesting systems

Merrett

Al-Hashimi

2017

Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE), 2017

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Abstract-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 energydriven 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.

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Power neutral performance scaling for energy harvesting MP-SoCs

Cited by 10 publications

References 11 publications

Energy-driven computing

Energy-driven computing

Enabling intermittent computing on high-performance out-of-order processors

Energy-driven computing: Rethinking the design of energy harvesting systems

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