Termination checking and task decomposition for task-based intermittent programs

Colin, Alexei; Lucia, Brandon

doi:10.1145/3178372.3179525

Cited by 60 publications

(74 citation statements)

References 32 publications

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“…DINO and Ratchet analyzed the idempotency and decomposed a program at compile-time using static code analysis [114], which imposes heavy burdens on the programmers. Fortunately, Colin et al developed CleanCut [112], a tool that can automatically decompose programs into efficient, idempotent tasks at compile-time. At run time, executions were performed task-by-task and a checkpointing process is launched after a task.…”

Section: Computing Optimizations For Batterylessmentioning

confidence: 99%

Sensing, Computing, and Communications for Energy Harvesting IoTs: A Survey

Lan

Hassan

et al. 2020

IEEE Commun. Surv. Tutorials

238

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Sensing Optimizations Computing Optimizations Communications Optimizations  Commercial products and services  Standards for wireless protocol and transducer test method  Checkpointing optimization  Timekeeping across power failures  Packet-less communication  Reinforcement learning based communication optimization  Backscatter communication  Kinetic EH sensing  Thermal EH sensing  Solar EH sensing  RF EH sensingTABLE I: Vendors specializing in energy harvesting components and solutions suitable for IoTs. Company Source Energy Harvester EH Solution Application Foundation Year Piezo Systems Kinetic × piezoelectric energy harvesting 1988 MIDE Technology Kinetic × piezoelectric energy harvesting

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Section: Computing Optimizations For Batterylessmentioning

confidence: 99%

Sensing, Computing, and Communications for Energy Harvesting IoTs: A Survey

Lan

Hassan

et al. 2020

IEEE Commun. Surv. Tutorials

238

View full text Add to dashboard Cite

show abstract

“…C3 Ensuring Task Termination: A static task decomposition model assumes that each task can execute to completion. If the hardware energy buffer provides inadequate energy to execute each task to completion, then a program will not terminate [13]. This leads to a third challenge: How do we enable the dynamic execution model to progress on a sub-task level?…”

Section: Drawbacks Of Static Task Decompositionmentioning

confidence: 99%

“…The effectiveness of the WEG hinges on correctly identifying the weight of each task, which WEG assumes is statically available. Profiling the time and energy cost of tasks in a program is a difficult, orthogonal problem [2,13]. WEG could use the result of an arbitrarily sophisticated profiling procedure.…”

Section: Task Coalescingmentioning

confidence: 99%

“…In contrast to Coala's construction of coalesced tasks at runtime, prior work has proposed to optimize task size at compile time. CleanCut [13] program analysis statically estimates energy consumption and splits the program into tasks until all tasks consume less energy than the device can store. An alternative program analysis generates different versions of a program with different task sizes and empirically selects the best among them [2].…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Untitled

2019

ACM Trans. Sen. Netw.

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Energy-neutral Internet of Things requires freeing embedded devices from batteries and powering them from ambient energy. Ambient energy is, however, unpredictable and can only power a device intermittently. Therefore, the paradigm of intermittent execution is to save the program state into non-volatile memory frequently to preserve the execution progress. In task-based intermittent programming, the state is saved at task transition. Tasks are fixed at compile time and agnostic to energy conditions. Thus, the state may be saved either more often than necessary or not often enough for the program to progress and terminate. To address these challenges, we propose Coala, an adaptive and efficient task-based execution model. Coala progresses on a multi-task scale when energy permits and preserves the computation progress on a sub-task scale if necessary. Coala's specialized memory virtualization mechanism ensures that power failures do not leave the program state in non-volatile memory inconsistent. Our evaluation on a real energy-harvesting platform not only shows that Coala reduces runtime by up to 54% as compared to a state-of-the-art system, but also it is able to progress where static systems fail. CCS Concepts: • Hardware → Power and energy; • Software and its engineering → Embedded software; Virtual memory;

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“…We provide a programming model that allows a programmer to develop an intermittent learning application that executes correctly on an intermittent system. Like many existing proposals, we adopt a task-based -which we call action-based -intermittent programming model [14,15,35,56,57,87]. We provide application programmers with an energy pre-inspection tool that helps them split an existing application code into sub-modules called actions that atomically run to completion on intermittently-powered systems.…”

Section: Introductionmentioning

confidence: 99%

Intermittent Learning

Lee

Islam

Luo

et al. 2019

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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This paper introduces intermittent learning -the goal of which is to enable energy harvested computing platforms capable of executing certain classes of machine learning tasks effectively and efficiently. We identify unique challenges to intermittent learning relating to the data and application semantics of machine learning tasks, and to address these challenges, we devise 1) an algorithm that determines a sequence of actions to achieve the desired learning objective under tight energy constraints, and 2) propose three heuristics that help an intermittent learner decide whether to learn or discard training examples at run-time which increases the energy efficiency of the system. We implement and evaluate three intermittent learning applications that learn the 1) air quality, 2) human presence, and 3) vibration using solar, RF, and kinetic energy harvesters, respectively. We demonstrate that the proposed framework improves the energy efficiency of a learner by up to 100% and cuts down the number of learning examples by up to 50% when compared to state-of-the-art intermittent computing systems that do not implement the proposed intermittent learning framework.

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Termination checking and task decomposition for task-based intermittent programs

Cited by 60 publications

References 32 publications

Sensing, Computing, and Communications for Energy Harvesting IoTs: A Survey

Sensing, Computing, and Communications for Energy Harvesting IoTs: A Survey

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Intermittent Learning

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