The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Going Deeper with Embedded FPGA Platform for Convolutional Neural Network
Abstract
In recent years, convolutional neural network (CNN) based methods have achieved great success in a large number of applications and have been among the most powerful and widely used techniques in computer vision. However, CNN-based methods are com-putational-intensive and resource-consuming, and thus are hard to be integrated into embedded systems such as smart phones, smart glasses, and robots. FPGA is one of the most promising platforms for accelerating CNN, but the limited bandwidth and on-chip memory size limit the performance of FPGA accelerator for CNN.
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Cites
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Cited by
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Han, S., Liu, X., Mao, H., Pu, J., Pedram, A., Horowitz, M. A., & Dally, W. J. (2016) · ACM SIGARCH Computer Architecture News · doi:10.1145/3007787.3001163 · Cited by 1,937
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Edge Computing for Autonomous Driving: Opportunities and Challenges
Liu, S., Liu, L., Tang, J., Yu, B., Wang, Y., & Shi, W. (2019) · Proceedings of the IEEE · doi:10.1109/jproc.2019.2915983 · Cited by 778
Self-citationESE: Efficient Speech Recognition Engine with Sparse LSTM on FPGA
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Self-citationA review of object detection based on deep learning
Xiao, Y., Tian, Z., Yu, J., Zhang, Y., Liu, S., Du, S., & Lan, X. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-08976-6 · Cited by 584
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Metadata
- Authors
- Qiu, J., Wang, J., Yao, S., Guo, K., Li, B., Zhou, E., Yu, J., Tang, T., Xu, N., Song, S., Wang, Y., & Yang, H.
- Venue
- Proceedings of the 2016 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays
- Publisher
- ACM
- Published
- 2016-02-21
- Pages
- 26-35
- OpenAlex
- W2276486856
- Cited by
- 1,252
- References
- 32
- FWCI
- 135.76
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Qiu, J., Wang, J., Yao, S., Guo, K., Li, B., Zhou, E., Yu, J., Tang, T., Xu, N., Song, S., Wang, Y., & Yang, H. (2016). Going Deeper with Embedded FPGA Platform for Convolutional Neural Network. Proceedings of the 2016 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays. https://doi.org/10.1145/2847263.2847265
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