Exploring Deep Spiking Neural Networks for Automated Driving Applications

Sambit Mohapatra, Heinrich Gotzig, Senthil Yogamani, Stefan Milz, Raoul Zöllner

2019

Abstract

Neural networks have become the standard model for various computer vision tasks in automated driving including semantic segmentation, moving object detection, depth estimation, visual odometry, etc. The main flavors of neural networks which are used commonly are convolutional (CNN) and recurrent (RNN). In spite of rapid progress in embedded processors, power consumption and cost is still a bottleneck. Spiking Neural Networks (SNNs) are gradually progressing to achieve low-power event-driven hardware architecture which has a potential for high efficiency. In this paper, we explore the role of deep spiking neural networks (SNN) for automated driving applications. We provide an overview of progress on SNN and argue how it can be a good fit for automated driving applications.

Paper Citation

in Harvard Style

Mohapatra S., Gotzig H., Yogamani S., Milz S. and Zöllner R. (2019). Exploring Deep Spiking Neural Networks for Automated Driving Applications. In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 5: VISAPP; ISBN 978-989-758-354-4, SciTePress, pages 548-555. DOI: 10.5220/0007469405480555

in Bibtex Style

@conference{visapp19,
author={Sambit Mohapatra and Heinrich Gotzig and Senthil Yogamani and Stefan Milz and Raoul Zöllner},
title={Exploring Deep Spiking Neural Networks for Automated Driving Applications},
booktitle={Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 5: VISAPP},
year={2019},
pages={548-555},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007469405480555},
isbn={978-989-758-354-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 5: VISAPP
TI - Exploring Deep Spiking Neural Networks for Automated Driving Applications
SN - 978-989-758-354-4
AU - Mohapatra S.
AU - Gotzig H.
AU - Yogamani S.
AU - Milz S.
AU - Zöllner R.
PY - 2019
SP - 548
EP - 555
DO - 10.5220/0007469405480555
PB - SciTePress