Thermal-Aware Servers for Real-Time Tasks on Multi-Core GPU-Integrated Embedded Systems

Hosseinimotlagh, S. N.; Kim, Hyoseung

doi:10.1109/rtas.2019.00029

Cited by 13 publications

(2 citation statements)

References 34 publications

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“…The scheduling of tasks within the periodic server in an intermittently-powered device should be done in a non-preemptive manner and is different from that in conventional real-time systems. Thus, existing hierarchical schedulability analysis for preemptive tasks in periodic servers [16,11,12,8] are inapplicable to our problem. Instead, we formulate this as a variant of the bin-packing problem with additional constraints.…”

Section: Multi-task Scheduling and Analysismentioning

confidence: 99%

Energy scheduling for task execution on intermittently-powered devices

Karimi

Kim

2020

SIGBED Rev.

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Intermittently-powered embedded devices (IPDs) are getting wide-spread attention these days. However, running periodic real-time tasks on these devices remains a challenging problem due to the lack of support for data freshness guarantees, timekeeping, and schedulability analysis. Especially, while many sensing tasks require long atomic operations for data acquisition from sensors, most prior work on IPDs assumes compute-only workloads and disregards such sensor operations. In this paper, we present a new energy scheduling scheme to execute periodic real-time tasks with atomic sensing operations. Our scheme keeps track of time and ensures the periodic execution of sensing tasks while efficiently utilizing intermittent power sources. We provide schedulability analysis to determine if a task is schedulable in a given charging setup, and extend this idea for scheduling multiple tasks. As a proof-of-concept, we design a custom programmable RFID tag device, called R'tag, and demonstrate the effectiveness of our proposed techniques in a realistic sensing application. We compare the baseline approach and the proposed scheme in both simulation and real platforms. Experimental results show that the proposed method outperforms the baseline approach in terms of task scheduling, timekeeping, and periodic sensing.

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Section: Multi-task Scheduling and Analysismentioning

confidence: 99%

Energy scheduling for task execution on intermittently-powered devices

Karimi

Kim

2020

SIGBED Rev.

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“…Due to increased interest in GPU for accelerating parallel real-time applications, many real-time scheduling frameworks for GPU have been proposed in recent years [27,45,21,37], with a particular focus on DNN acceleration [76,69]. We first review works concerned with kernel scheduling, leaving more directly-related frameworks focusing on memory management to Section 2.3.3.…”

Section: Real-time Framework For Gpumentioning

confidence: 99%