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» Dynamic power management using machine learning
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ICML
2007
IEEE
15 years 10 months ago
Hierarchical Gaussian process latent variable models
The Gaussian process latent variable model (GP-LVM) is a powerful approach for probabilistic modelling of high dimensional data through dimensional reduction. In this paper we ext...
Neil D. Lawrence, Andrew J. Moore
SOCIALCOM
2010
14 years 7 months ago
Using Text Analysis to Understand the Structure and Dynamics of the World Wide Web as a Multi-Relational Graph
A representation of the World Wide Web as a directed graph, with vertices representing web pages and edges representing hypertext links, underpins the algorithms used by web search...
Harish Sethu, Alexander Yates
DAC
2006
ACM
15 years 10 months ago
High-level power management of embedded systems with application-specific energy cost functions
Most existing dynamic voltage scaling (DVS) schemes for multiple tasks assume an energy cost function (energy consumption versus execution time) that is independent of the task ch...
Youngjin Cho, Naehyuck Chang, Chaitali Chakrabarti...
ISCA
2011
IEEE
269views Hardware» more  ISCA 2011»
14 years 1 months ago
Power management of online data-intensive services
Much of the success of the Internet services model can be attributed to the popularity of a class of workloads that we call Online Data-Intensive (OLDI) services. These workloads ...
David Meisner, Christopher M. Sadler, Luiz Andr&ea...
ISCA
2008
IEEE
89views Hardware» more  ISCA 2008»
15 years 3 months ago
Variation-Aware Application Scheduling and Power Management for Chip Multiprocessors
Within-die process variation causes individual cores in a Chip Multiprocessor (CMP) to differ substantially in both static power consumed and maximum frequency supported. In this ...
Radu Teodorescu, Josep Torrellas