Survey of convolutional neural network accelerators on field-programmable gate array platforms: architectures and optimization techniques-Reference-Cited by-同舟云学术

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Survey of convolutional neural network accelerators on field-programmable gate array platforms: architectures and optimization techniques

Published:2024-03-29 Issue:3 Volume:21 Page:
ISSN:1861-8200
Container-title:Journal of Real-Time Image Processing
language:en
Short-container-title:J Real-Time Image Proc

Author:

Hong Hyeonseok,Choi Dahun,Kim Namjoon,Lee Haein,Kang Beomjin,Kang Huibeom,Kim Hyun

Funder

Ministry of Science and ICT, South Korea

National Research Foundation of Korea

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11554-024-01442-8.pdf

Reference83 articles.

1. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

2. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2017)

3. Choi, J., Chun, D., Kim, H., Lee, H.-J.: Gaussian yolov3: an accurate and fast object detector using localization uncertainty for autonomous driving. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 502–511 (2019)

4. Redmon, J., Farhadi, A.: Yolov3: An Incremental Improvement. arXiv preprint arXiv:1804.02767 (2018)

5. Bolya, D., Zhou, C., Xiao, F., Lee, Y.J.: Yolact: real-time instance segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9157–9166 (2019)

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