The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Improving the Performance of OpenCL-based FPGA Accelerator for Convolutional Neural Network
Abstract
OpenCL FPGA has recently gained great popularity with emerging needs for workload acceleration such as Convolutional Neural Network (CNN), which is the most popular deep learning architecture in the domain of computer vision. While OpenCL enhances the code portability and programmability of FPGA, it comes at the expense of performance. The key challenge is to optimize the OpenCL kernels to efficiently utilize the flexible hardware resources in FPGA. Simply optimizing the OpenCL kernel code through various compiler options turns out insufficient to achieve desirable performance for both compute-intensive and data-intensive workloads such as convolutional neural networks.
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Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
A dynamically configurable coprocessor for convolutional neural networks
Chakradhar, S., Sankaradas, M., Jakkula, V., & Cadambi, S. (2010) · Proceedings of the 37th annual international symposium on Computer architecture · doi:10.1145/1815961.1815993 · Cited by 343
Cited by
Automated Systolic Array Architecture Synthesis for High Throughput CNN Inference on FPGAs
Wei, X., Yu, C. H., Zhang, P., Chen, Y., Wang, Y., Hu, H., Liang, Y., & Cong, J. (2017) · Proceedings of the 54th Annual Design Automation Conference 2017 · doi:10.1145/3061639.3062207 · Cited by 426
[DL] A Survey of FPGA-based Neural Network Inference Accelerators
Guo, K., Zeng, S., Yu, J., Wang, Y., & Yang, H. (2019) · ACM Transactions on Reconfigurable Technology and Systems · doi:10.1145/3289185 · Cited by 292
Caffeine: Toward Uniformed Representation and Acceleration for Deep Convolutional Neural Networks
Zhang, C., Sun, G., Fang, Z., Zhou, P., Pan, P., & Cong, J. (2019) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2017.2785257 · Cited by 270
Defect detection in printed circuit boards using you-only-look-once convolutional neural networks
Adibhatla, V. A., Chih, H.-C., Hsu, C.-C., Cheng, J., Abbod, M. F., & Shieh, J.-S. (2020) · Electronics · doi:10.3390/electronics9091547 · Cited by 179
Accelerating CNN inference on FPGAs: A Survey
Abdelouahab, K., Pelcat, M., Serot, J., & Berry, F. (2018) · arXiv · doi:10.48550/arxiv.1806.01683 · Cited by 107
Metadata
- Authors
- Zhang, J., & Li, J.
- Venue
- Proceedings of the 2017 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays
- Publisher
- ACM
- Published
- 2017-02-22
- Pages
- 25-34
- OpenAlex
- W2585774018
- Cited by
- 208
- References
- 3
- FWCI
- 27.72
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Zhang, J., & Li, J. (2017). Improving the Performance of OpenCL-based FPGA Accelerator for Convolutional Neural Network. Proceedings of the 2017 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays. https://doi.org/10.1145/3020078.3021698
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