Automatic Tuning of Interactive Perception Applications

Qian Zhu, Branislav Kveton, Lily Mummert, Padmanabhan Pillai
Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:742-750, 2010.

Abstract

Interactive applications incorporating high-data rate sensing and computer vision are becoming possible due to novel runtime systems and the use of parallel computation resources. To allow interactive use, such applications require careful tuning of multiple application parameters to meet required fidelity and latency bounds. This is a nontrivial task, often requiring expert knowledge, which becomes intractable as resources and application load characteristics change. This paper describes a method for automatic performance tuning that learns application characteristics and effects of tunable parameters online, and constructs models that are used to maximize fidelity for a given latency constraint. The paper shows that accurate latency models can be learned online, knowledge of application structure can be used to reduce the complexity of the learning task, and operating points can be found that achieve 90% of the optimal fidelity by exploring the parameter space only 3% of the time.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR8-zhu10a, title = {Automatic Tuning of Interactive Perception Applications}, author = {Zhu, Qian and Kveton, Branislav and Mummert, Lily and Pillai, Padmanabhan}, booktitle = {Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence}, pages = {742--750}, year = {2010}, editor = {Grünwald, Peter and Spirtes, Peter}, volume = {R8}, series = {Proceedings of Machine Learning Research}, month = {08--11 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r8/main/assets/zhu10a/zhu10a.pdf}, url = {https://proceedings.mlr.press/r8/zhu10a.html}, abstract = {Interactive applications incorporating high-data rate sensing and computer vision are becoming possible due to novel runtime systems and the use of parallel computation resources. To allow interactive use, such applications require careful tuning of multiple application parameters to meet required fidelity and latency bounds. This is a nontrivial task, often requiring expert knowledge, which becomes intractable as resources and application load characteristics change. This paper describes a method for automatic performance tuning that learns application characteristics and effects of tunable parameters online, and constructs models that are used to maximize fidelity for a given latency constraint. The paper shows that accurate latency models can be learned online, knowledge of application structure can be used to reduce the complexity of the learning task, and operating points can be found that achieve 90% of the optimal fidelity by exploring the parameter space only 3% of the time.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Automatic Tuning of Interactive Perception Applications %A Qian Zhu %A Branislav Kveton %A Lily Mummert %A Padmanabhan Pillai %B Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2010 %E Peter Grünwald %E Peter Spirtes %F pmlr-vR8-zhu10a %I PMLR %P 742--750 %U https://proceedings.mlr.press/r8/zhu10a.html %V R8 %X Interactive applications incorporating high-data rate sensing and computer vision are becoming possible due to novel runtime systems and the use of parallel computation resources. To allow interactive use, such applications require careful tuning of multiple application parameters to meet required fidelity and latency bounds. This is a nontrivial task, often requiring expert knowledge, which becomes intractable as resources and application load characteristics change. This paper describes a method for automatic performance tuning that learns application characteristics and effects of tunable parameters online, and constructs models that are used to maximize fidelity for a given latency constraint. The paper shows that accurate latency models can be learned online, knowledge of application structure can be used to reduce the complexity of the learning task, and operating points can be found that achieve 90% of the optimal fidelity by exploring the parameter space only 3% of the time. %Z Reissued by PMLR on 04 October 2026.
APA
Zhu, Q., Kveton, B., Mummert, L. & Pillai, P.. (2010). Automatic Tuning of Interactive Perception Applications. Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R8:742-750 Available from https://proceedings.mlr.press/r8/zhu10a.html. Reissued by PMLR on 04 October 2026.

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