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    Rights statement: © ACM, 2018. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Proceedings of 19th ACM SIGPLAN/SIGBED Conference on Languages, Compilers, and Tools for Embedded Systems (LCTES’18). http://dx.doi.org/10.1145/3211332.3211336

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Adaptive Deep Learning Model Selection on Embedded Systems

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paper

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Adaptive Deep Learning Model Selection on Embedded Systems. / Taylor, Ben; Sanz Marco, Vicent; Wolff, Willy; Elkhatib, Yehia; Wang, Zheng.

LCTES 2018 Proceedings of the 19th ACM SIGPLAN/SIGBED International Conference on Languages, Compilers, and Tools for Embedded Systems. New York : ACM, 2018. p. 31-43.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paper

Harvard

Taylor, B, Sanz Marco, V, Wolff, W, Elkhatib, Y & Wang, Z 2018, Adaptive Deep Learning Model Selection on Embedded Systems. in LCTES 2018 Proceedings of the 19th ACM SIGPLAN/SIGBED International Conference on Languages, Compilers, and Tools for Embedded Systems. ACM, New York, pp. 31-43. https://doi.org/10.1145/3211332.3211336

APA

Taylor, B., Sanz Marco, V., Wolff, W., Elkhatib, Y., & Wang, Z. (2018). Adaptive Deep Learning Model Selection on Embedded Systems. In LCTES 2018 Proceedings of the 19th ACM SIGPLAN/SIGBED International Conference on Languages, Compilers, and Tools for Embedded Systems (pp. 31-43). ACM. https://doi.org/10.1145/3211332.3211336

Vancouver

Taylor B, Sanz Marco V, Wolff W, Elkhatib Y, Wang Z. Adaptive Deep Learning Model Selection on Embedded Systems. In LCTES 2018 Proceedings of the 19th ACM SIGPLAN/SIGBED International Conference on Languages, Compilers, and Tools for Embedded Systems. New York: ACM. 2018. p. 31-43 https://doi.org/10.1145/3211332.3211336

Author

Taylor, Ben ; Sanz Marco, Vicent ; Wolff, Willy ; Elkhatib, Yehia ; Wang, Zheng. / Adaptive Deep Learning Model Selection on Embedded Systems. LCTES 2018 Proceedings of the 19th ACM SIGPLAN/SIGBED International Conference on Languages, Compilers, and Tools for Embedded Systems. New York : ACM, 2018. pp. 31-43

Bibtex

@inproceedings{d01fc8b2d3bb46f3bacac3ab72d1f81f,
title = "Adaptive Deep Learning Model Selection on Embedded Systems",
abstract = "The recent ground-breaking advances in deep learning networks (DNNs) make them attractive for embedded systems. However, it can take a long time for DNNs to make an inference on resource-limited embedded devices. Offloading the computation into the cloud is often infeasible due to privacy concerns, high latency, or the lack of connectivity. As such, there is a critical need to find a way to effectively execute the DNN models locally on the devices.This paper presents an adaptive scheme to determine which DNN model to use for a given input, by considering the desired accuracy and inference time. Our approach employs machine learning to develop a predictive model to quickly select a pre-trained DNN to use for a given input andthe optimization constraint. We achieve this by first training off-line a predictive model, and then use the learnt model to select a DNN model to use for new, unseen inputs. We apply our approach to the image classification task and evaluate it on a Jetson TX2 embedded deep learning platform using the ImageNet ILSVRC 2012 validation dataset. We consider a range of influential DNN models. Experimental results show that our approach achieves a 7.52% improvement ininference accuracy, and a 1.8x reduction in inference time over the most-capable, single DNN model.",
author = "Ben Taylor and {Sanz Marco}, Vicent and Willy Wolff and Yehia Elkhatib and Zheng Wang",
note = "{\textcopyright} ACM, 2018. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Proceedings of 19th ACM SIGPLAN/SIGBED Conference on Languages, Compilers, and Tools for Embedded Systems (LCTES{\textquoteright}18). http://dx.doi.org/10.1145/3211332.3211336 ",
year = "2018",
month = jun,
day = "19",
doi = "10.1145/3211332.3211336",
language = "English",
isbn = "9781450358033",
pages = "31--43",
booktitle = "LCTES 2018 Proceedings of the 19th ACM SIGPLAN/SIGBED International Conference on Languages, Compilers, and Tools for Embedded Systems",
publisher = "ACM",

}

RIS

TY - GEN

T1 - Adaptive Deep Learning Model Selection on Embedded Systems

AU - Taylor, Ben

AU - Sanz Marco, Vicent

AU - Wolff, Willy

AU - Elkhatib, Yehia

AU - Wang, Zheng

N1 - © ACM, 2018. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Proceedings of 19th ACM SIGPLAN/SIGBED Conference on Languages, Compilers, and Tools for Embedded Systems (LCTES’18). http://dx.doi.org/10.1145/3211332.3211336

PY - 2018/6/19

Y1 - 2018/6/19

N2 - The recent ground-breaking advances in deep learning networks (DNNs) make them attractive for embedded systems. However, it can take a long time for DNNs to make an inference on resource-limited embedded devices. Offloading the computation into the cloud is often infeasible due to privacy concerns, high latency, or the lack of connectivity. As such, there is a critical need to find a way to effectively execute the DNN models locally on the devices.This paper presents an adaptive scheme to determine which DNN model to use for a given input, by considering the desired accuracy and inference time. Our approach employs machine learning to develop a predictive model to quickly select a pre-trained DNN to use for a given input andthe optimization constraint. We achieve this by first training off-line a predictive model, and then use the learnt model to select a DNN model to use for new, unseen inputs. We apply our approach to the image classification task and evaluate it on a Jetson TX2 embedded deep learning platform using the ImageNet ILSVRC 2012 validation dataset. We consider a range of influential DNN models. Experimental results show that our approach achieves a 7.52% improvement ininference accuracy, and a 1.8x reduction in inference time over the most-capable, single DNN model.

AB - The recent ground-breaking advances in deep learning networks (DNNs) make them attractive for embedded systems. However, it can take a long time for DNNs to make an inference on resource-limited embedded devices. Offloading the computation into the cloud is often infeasible due to privacy concerns, high latency, or the lack of connectivity. As such, there is a critical need to find a way to effectively execute the DNN models locally on the devices.This paper presents an adaptive scheme to determine which DNN model to use for a given input, by considering the desired accuracy and inference time. Our approach employs machine learning to develop a predictive model to quickly select a pre-trained DNN to use for a given input andthe optimization constraint. We achieve this by first training off-line a predictive model, and then use the learnt model to select a DNN model to use for new, unseen inputs. We apply our approach to the image classification task and evaluate it on a Jetson TX2 embedded deep learning platform using the ImageNet ILSVRC 2012 validation dataset. We consider a range of influential DNN models. Experimental results show that our approach achieves a 7.52% improvement ininference accuracy, and a 1.8x reduction in inference time over the most-capable, single DNN model.

U2 - 10.1145/3211332.3211336

DO - 10.1145/3211332.3211336

M3 - Conference contribution/Paper

SN - 9781450358033

SP - 31

EP - 43

BT - LCTES 2018 Proceedings of the 19th ACM SIGPLAN/SIGBED International Conference on Languages, Compilers, and Tools for Embedded Systems

PB - ACM

CY - New York

ER -