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

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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“…In order to differentiate and evaluate existing works, this section presents a taxonomy based on the following three classes of IC methods [40]:…”

Section: (C) Taxonomymentioning

confidence: 99%

Energy-driven computing

Sliper

Cetinkaya

Weddell

et al. 2019

Phil. Trans. R. Soc. A.

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“…As already mentioned, when considering systems powered by energy harvesters, it is important to accommodate the uncontrollable, and often intermittent, nature of the source. Various approaches for intermittent computing exist in literature to enable system state retention [19,20]. Broadly, they can be classified into two categories: software-based approaches, in which the system state is saved into a Non-Volatile Memory (NVM) before a power failure occurs and restored once the power supply recovers, and architectural approaches, in which the entire system is designed is to be non-volatile (e.g.…”

Section: Energy Harvesting Iot System Designmentioning

confidence: 99%

Health Monitoring Based on Internet of Things (IoT)

Balsamo¹,

Das²

2019

Health Monitoring Systems

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The chapter gives a brief introduction of the technological standards, challenges and open research areas in internet of things (IoT) based health monitoring. In particular, low energy implementation, signal processing and machine learning challenges, communication constraint and security concerns have been reviewed. This is a growing field of research both in academia and in industry and timely topic for focussed cross-disciplinary research to address the electronic and remote healthcare challenges.

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“…Depending on the control strategy, transient computing offers task-based and reactive schemes (Sec. II) [5]. In task-based schemes, the application is divided into small tasks (e.g., sensing, processing, and transmission), each of which has to be completed within one power cycle [6].…”

Section: Introductionmentioning

confidence: 99%

A Control Flow for Transiently Powered Energy Harvesting Sensor Systems

et al. 2020

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Transient computing enables application execution to be performed despite power outages. Although it handles the non-deterministic nature of energy harvesting (EH), sensor systems envisioned by the IoT seek more cost-and volume-effective solutions, which are better tailored to application requirements. Additionally, a major drawback of transient computing, keeping track of time, hinders its widespread adoption in the IoT. To overcome these challenges, this paper proposes a control flow for sensor systems by combining two state-of-the-art transient computing schemes in an energy-aware manner, underpinned by a strategy for timekeeping. It enables application execution to be reliably performed even under the most severe EH conditions, with an improved cost and volume efficiency, i.e., smaller energy storage. Benefiting from the combination of the two schemes, dynamic adjustment of system performance is achieved, while the time is accurately tracked. To illustrate the applicability of this flow to actual sensor systems, two case studies: a bicycle trip computer and a step counter, are presented. Empirical results reveal that, even with a tiny amount of energy harvested (tens of µJ), our proposed approach can meet application requirements with smaller storage, i.e., 40% and 66% reduction in required capacitance for the presented case studies.

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

Cited by 6 publications

References 18 publications

Energy-driven computing

Energy-driven computing

Health Monitoring Based on Internet of Things (IoT)

A Control Flow for Transiently Powered Energy Harvesting Sensor Systems

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