Don't burn your mobile!

Tilli, Andrea; Bartolini, Andrea; Cacciari, Matteo; Benini, Luca

doi:10.1145/2380445.2380504

Cited by 13 publications

(4 citation statements)

References 21 publications

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“…For the sake of completeness, additional tests related to Tilli et al [2012a] are reported in Section E of the Online Appendix.…”

Section: Resultsmentioning

confidence: 99%

“…1 Nevertheless, the considered approach can be applied to any system, either homogeneous or heterogeneous, where sprinting capabilities are foreseen, according to the system thermal capacitance. This work is inspired by the preliminary results reported in Tilli et al [2012a]. With respect to that work, a novel guaranteed resprinting formulation is introduced to manage variable scenarios, following a sort of "one-step-ahead" policy.…”

Section: Contributionmentioning

confidence: 99%

“…17 Characteristics of the selected model will affect also the feasibility and the correct formulation of the control problem. This issue is not discussed here; we refer the interest reader to Tilli et al [2012b] for further details. with sampling time T sT .…”

Section: Lower-layer Thermal Controllermentioning

confidence: 99%

“…According to the characteristics of the considered thermal system, if the silicon temperature is capped at T max , the T PCM cannot be larger than T max[Tilli et al 2012b]. …”

mentioning

confidence: 99%

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Guaranteed Computational Resprinting via Model-Predictive Control

Tilli

Bartolini

Cacciari

et al. 2015

ACM Trans. Embed. Comput. Syst.

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Today and future many-core systems are facing the utilization wall and dark silicon problems, for which not all the processing engines can be powered at the same time as this will lead to a power consumption higher than the Total Design Power (TDP) budget. Recently, computational sprinting approaches addressed the problem by exploiting the intrinsic thermal capacitance of the chip and the properties of common applications, which require intense, but temporary, use of resources. The thermal capacitance, possibly augmented with phase change materials, enables the temporary activation of all the resources simultaneously, although they largely exceed the steady-state thermal design power. In this article, we present an innovative and lowoverhead hierarchical model-predictive controller for managing thermally safe sprinting with predictable resprinting rate, which ensures the correct execution of mixed-criticality tasks. Well-targeted simulations, also based on real workload benchmarks, show the applicability and the effectiveness of our solution. . 2015. Guaranteed computational resprinting via model-predictive control.

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“…For the sake of completeness, additional tests related to Tilli et al [2012a] are reported in Section E of the Online Appendix.…”

Section: Resultsmentioning

confidence: 99%

Section: Contributionmentioning

confidence: 99%

Section: Lower-layer Thermal Controllermentioning

confidence: 99%

“…According to the characteristics of the considered thermal system, if the silicon temperature is capped at T max , the T PCM cannot be larger than T max[Tilli et al 2012b]. …”

mentioning

confidence: 99%

See 2 more Smart Citations

Guaranteed Computational Resprinting via Model-Predictive Control

Tilli

Bartolini

Cacciari

et al. 2015

ACM Trans. Embed. Comput. Syst.

Self Cite

View full text Add to dashboard Cite

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Trusted, Heterogeneous, and Autonomic Mobile Cloud

Lombardi

Pietro

2016

Secure System Design and Trustable Computing

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Offloading computing to distributed and possibly mobile nodes is increasingly popular thanks to the convenience and availability of cloud resources. However, trusted mobile computing is not presently viable due to a number of issues in both the mobile platform architectures and in the cloud service implementations. The complexity of such systems potentially exposes them to malicious and/or selfish behavior. This chapter describes the state-of-the-art research on theoretical advancements and practical implementations of trusted computing on a mobile cloud. Further, mobile distributed cloud computing security and reliability issues are introduced. Discussed solutions feature different levels of resiliency against malicious and misbehaving nodes.

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