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原文摘要

Intermittent computing nodes run on harvested energy, which they store in capacitors and spend in short bursts of computation. Choosing those capacitors is a trade-off: large capacitors can power large tasks but leak more current, wasting harvested energy, while small capacitors leak less but cannot power large tasks. Recent work, LEACS, showed that selecting capacitors and mapping tasks to them at compile time improves inference throughput. However, LEACS optimizes the mapping for a predetermined workload, whereas sensor nodes often run data-dependent workloads in which the next task depends on the outcome of earlier ones. We present Thimble, an energy storage system that maps a node's tasks to capacitors at runtime. Thimble models the workload as a Markov chain over tasks and assigns each task to a capacitor by weighing capacitor leakage, the task's energy demand and, when it replans, the likelihood of each future task. We compare Thimble against a reimplementation of LEACS, which ranks every static plan by simulation. When the training trace resembles deployment, Thimble's single design-time solve comes within 0.3--4% of LEACS's throughput. When the deployment conditions drift from the characteristics of the training set, LEACS's throughput-focused static plans can fail sharply. On the lowest-power source we consider, LEACS's plan stalls on deployment traces that harvest less than its training trace, and on some never wakes; Thimble avoids this, with 22--29% higher throughput at the reference workload. A sensitivity sweep over 25 workload profiles on that source shows that Thimble with depth replanning leads LEACS by 22--42% throughout, and what run-time remapping adds: replanning at a fixed interval matches or improves on the static map at 21 of 25 points, by up to 7%.