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Dynamic voltage scaling on a low-power microprocessor

Pouwelse, Johan, Langendoen, Koen, Sips, Henk·2001·Proceedings of the 7th annual international conference on Mobile computing and networking

Abstract

Power consumption is the limiting factor for the functionality of future wearable devices. Since interactive applications like wireless information access generate bursts of activities, it is important to match the performance of the wearable device accordingly. This paper describes a system with a microprocessor whose speed can be varied (frequency scaling) as well as its supply voltage. Voltage scaling is important for reducing power consumption to very low values when operating at low speeds. Measurements show that the energy per instruction at minimal speed is 1/5 of the energy required at full speed. The frequency and voltage can be scaled dynamically from user space in only 140 μs. This allows power-aware applications to quickly adjust the performance level of the processor whenever the workload changes. Experiments with an H.263 video benchmark show that the power-aware decoder outperforms a static fixed-frequency policy as well as a dynamic interval-based scheduler.

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Metadata

Authors
Pouwelse, J., Langendoen, K., & Sips, H.
Venue
Proceedings of the 7th annual international conference on Mobile computing and networking
Publisher
ACM
Published
2001-07-16
Pages
251-259
ISSN
1543-5679 (ISSN-L)
OpenAlex
W2004400944
Cited by
343
References
0
FWCI
16.70
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: Dynamic voltage scaling on a low-power microprocessor. https://app.cochat.ai/papers/10.1145/381677.381701
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Pouwelse, J., Langendoen, K., & Sips, H. (2001). Dynamic voltage scaling on a low-power microprocessor. Proceedings of the 7th annual international conference on Mobile computing and networking. https://doi.org/10.1145/381677.381701

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.