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The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.

A survey of design techniques for system-level dynamic power management

Open access
Benini, L., Bogliolo, A., De Micheli, G.·2000·IEEE Transactions on Very Large Scale Integration (VLSI) Systems

Abstract

Dynamic power management (DPM) is a design methodology for dynamically reconfiguring systems to provide the requested services and performance levels with a minimum number of active components or a minimum load on such components. DPM encompasses a set of techniques that achieves energy-efficient computation by selectively turning off (or reducing the performance of) system components when they are idle (or partially unexploited). In this paper, we survey several approaches to system-level dynamic power management. We first describe how systems employ power-manageable components and how the use of dynamic reconfiguration can impact the overall power consumption. We then analyze DPM implementation issues in electronic systems, and we survey recent initiatives in standardizing the hardware/software interface to enable software-controlled power management of hardware components.

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Metadata

Authors
Benini, L., Bogliolo, A., & De Micheli, G.
Venue
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Published
2000-06
Volume
8
Issue
3
Pages
299-316
ISSN
  • 1063-8210 (ISSN-L)
  • 1557-9999
OpenAlex
W2136244995
Cited by
1,241
References
25
FWCI
56.22
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: A survey of design techniques for system-level dynamic power management. https://app.cochat.ai/papers/10.1109/92.845896
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Benini, L., Bogliolo, A., & De Micheli, G. (2000). A survey of design techniques for system-level dynamic power management. IEEE Transactions on Very Large Scale Integration (VLSI) Systems. https://doi.org/10.1109/92.845896

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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.