Predicting Failures in Embedded Systems Using Long Short-Term Inference

Zhang Tianyi; Seo Minjun; Donyanavard Bryan; Dutt Nikil; Kurdahi Fadi

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Predicting Failures in Embedded Systems Using Long Short-Term Inference

机译：使用长短短期推断预测嵌入式系统中的故障

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Users of embedded and cyber-physical systems expect dependable operation for an increasingly diverse set of applications and environments. Reactive self-diagnosis techniques either use unnecessarily conservative guardbands, or do not prevent catastrophic failures. In this letter, we utilize machine-learning techniques to design a prediction engine in order to predict failures on-device in the embedded systems. We evaluate our prediction engine's effectiveness for predicting temperature behavior on a mobile system-on-chip and propose a realizable hardware implementation for the use-case.

机译：嵌入式和网络物理系统的用户期望越来越多样化的应用程序和环境的可靠运行。无功自诊断技术要么使用不必要的保守保护带，或者不会阻止灾难性失败。在这封信中，我们利用机器学习技术来设计预测引擎，以便在嵌入式系统中预测设备上的故障。我们评估我们的预测引擎，以预测芯片系统上的温度行为的效果，并提出用于用例的可实现的硬件实现。

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来源
《Embedded Systems Letters, IEEE》 |2021年第3期|85-89|共5页
作者
Zhang Tianyi; Seo Minjun; Donyanavard Bryan; Dutt Nikil; Kurdahi Fadi;
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作者单位

Harbin Inst Technol Sch Astronaut Harbin 150001 Peoples R China;

Univ Calif Irvine Ctr Embedded & Cyber Phys Syst Irvine CA 92697 USA;

Univ Calif Irvine Ctr Embedded & Cyber Phys Syst Irvine CA 92697 USA;

Univ Calif Irvine Ctr Embedded & Cyber Phys Syst Irvine CA 92697 USA;

Univ Calif Irvine Ctr Embedded & Cyber Phys Syst Irvine CA 92697 USA;

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正文语种 eng
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关键词
Predictive models; Temperature measurement; Temperature distribution; Data models; Load modeling; Embedded systems; Hardware; Embedded systems; managed runtime; model prediction; recurrent neural networks; runtime;

机译：预测模型;温度测量;温度分布;数据模型;负载建模;嵌入式系统;硬件;嵌入式系统;托管运行时;模型预测;经常性神经网络;运行时间;

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Predicting Failures in Embedded Systems Using Long Short-Term Inference

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