The DOI matches its Crossref record; 118 of the 118 references it cites were found in a registry or an index.
A Survey on optimized implementation of deep learning models on the NVIDIA Jetson platform
Abstract
Abstract Design of hardware accelerators for neural network (NN) applications involves walking a tight rope amidst the constraints of low-power, high accuracy and throughput. NVIDIA’s Jetson is a promising platform for embedded machine learning which seeks to achieve a balance between the above objectives. In this paper, we provide a survey of works that evaluate and optimize neural network applications on Jetson platform. We review both hardware and algorithmic optimizations performed for running NN algorithms on Jetson and show the real-life applications where these algorithms have been applied. We also review the works that compare Jetson with similar platforms. While the survey focuses on Jetson as an exemplar embedded system, many of the ideas and optimizations will apply just as well to existing and future embedded systems. It is widely believed that the ability to run AI algorithms on low-cost, low-power platforms will be crucial for achieving the “AI for all” vision. This survey seeks to provide a glimpse of the recent progress towards that goal.
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
You Only Look Once: Unified, Real-Time Object Detection
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803
(2016) · Cited by 31,590
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2644615 · Cited by 19,631
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., & Adam, H. (2017) · arXiv · doi:10.48550/arxiv.1704.04861 · Cited by 19,015
Rapid object detection using a boosted cascade of simple features
Viola, P., & Jones, M. (2005) · Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001 · doi:10.1109/cvpr.2001.990517 · Cited by 18,300
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
(1993) · doi:10.1109/cvpr.1993 · Cited by 17,667
Distilling the Knowledge in a Neural Network
Hinton, G., Vinyals, O., & Dean, J. (2015) · arXiv · doi:10.48550/arxiv.1503.02531 · Cited by 14,204
Advances in Neural Information Processing Systems 29
Teymur, O., Zygalakis, K., & Calderhead, B. (2016) · Cited by 13,881
Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks
Zhang, K., Zhang, Z., Li, Z., & Qiao, Y. (2016) · IEEE Signal Processing Letters · doi:10.1109/lsp.2016.2603342 · Cited by 6,214
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A survey of techniques for optimizing deep learning on gpus
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Self-citationA survey on modeling and improving reliability of DNN algorithms and accelerators
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Self-citationA survey on deep transfer learning to edge computing for mitigating the COVID-19 pandemic
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Metadata
- Authors
- Mittal, S.
- Venue
- Journal of Systems Architecture
- Publisher
- Elsevier BV
- Published
- 2019-08
- Volume
- 97
- Pages
- 428-442
- ISSN
- 1383-7621 (ISSN-L)
- OpenAlex
- W2907882680
- Cited by
- 294
- References
- 118
- FWCI
- 15.31
- Type
- journal article
- Language
- en
- Open access
- closed
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