Peter Stone's Selected Publications

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Efficient Real-Time Inference in Temporal Convolution Networks

Efficient Real-Time Inference in Temporal Convolution Networks.
Piyush Khandelwal, James MacGlashan, Peter Wurman, and Peter Stone.
In Proceedings of the 2021 International Conference on Robotics and Automation (ICRA 2021), May 2021.

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Abstract

It has been recently demonstrated that Temporal Convolution Networks (TCNs) provide state-of-the-art results in many problem domains where the input data is a time-series. TCNs typically incorporate information from a long history of inputs (the receptive field) into a single output using many convolution layers. Real-time inference using a trained TCN can be challenging on devices with limited compute and memory, especially if the receptive field is large. This paper introduces the RT-TCN algorithm that reuses the output of prior convo- lution operations to minimize the computational requirements and persistent memory footprint of a TCN during real-time inference. We also show that when a TCN is trained using time slices of the input time-series, it can be executed in real- time continually using RT-TCN. In addition, we provide TCN architecture guidelines that ensure that real-time inference can be performed within memory and computational constraints.

BibTeX Entry

@inProceedings {ICRA21-Piyush,
	author = {Piyush Khandelwal and James MacGlashan and Peter Wurman and Peter Stone},
	title = {Efficient Real-Time Inference in Temporal Convolution Networks},
	booktitle = {Proceedings of the 2021 International Conference on Robotics and Automation (ICRA 2021)},
	location = {Xi’an China},
	month = {May},
	year = {2021},
	abstract = {
           It has been recently demonstrated that Temporal Convolution
           Networks (TCNs) provide state-of-the-art results in many
           problem domains where the input data is a time-series.
           TCNs typically incorporate information from a long history
           of inputs (the receptive field) into a single output using
           many convolution layers. Real-time inference using a
           trained TCN can be challenging on devices with limited
           compute and memory, especially if the receptive field is
           large. This paper introduces the RT-TCN algorithm that
           reuses the output of prior convo- lution operations to
           minimize the computational requirements and persistent
           memory footprint of a TCN during real-time inference. We
           also show that when a TCN is trained using time slices of
           the input time-series, it can be executed in real- time
           continually using RT-TCN. In addition, we provide TCN
           architecture guidelines that ensure that real-time
           inference can be performed within memory and computational
           constraints.
	},
}

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