The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
A Hybrid GPU-FPGA-based Computing Platform for Machine Learning
Open accessAbstract
We present a hybrid GPU-FPGA based computing platform to tackle the high-density computing problem of machine learning. In our platform, the training part of a machine learning application is implemented on GPU and the inferencing part is implemented on FPGA. It should also include a model transplantation part which can transplant the model from the training part to the inferencing part. For evaluating this design methodology, we selected the LeNet-5 as our benchmark algorithm. During the training phase, GPU TitanXp’s speed was about 8.8x faster than CPU E-1620 and in the inferencing phase, FPGA Arria-10’s inferencing speed was fastest, 44.4x faster than CPU E-1620 and 6341x faster than GPU TitanXp. Moreover, by adopting our design methodology, we improved our LeNet-5 machine learning model’s accuracy from 99.05% to 99.13%, and successfully preserved the accuracy (99.13%) when transplanting the model from the GPU platform to the FPGA platform.
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“For another work, a hybrid CPU-FPGA based computing platform for machine learning is proposed by [83] to implement the LeNet-5 machine learning algorithm.”
From Embedded Intelligence on FPGA: Survey, Applications and Challenges (2021) · cites this paper as [83]
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“The proposed solution indication that the training phase is handled by the GPU, whereas FPGA is used for the inferencing phase due to FPGA consisting of logic circuits that are able to fully optimize the performance of the hardware accelerator.”
From Embedded Intelligence on FPGA: Survey, Applications and Challenges (2021) · cites this paper as [83]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Based on the research, the FPGA Arria 10 speed is six times faster than GPU and 44 times faster than CPU in the inferencing process [83].”
From Embedded Intelligence on FPGA: Survey, Applications and Challenges (2021) · cites this paper as [83]
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In the citing paper
“In this Mid-range Arria, FPGA is adopted as each inference only requires a small quantity of data and less frequent communication with with host.”
From Embedded Intelligence on FPGA: Survey, Applications and Challenges (2021) · cites this paper as [83]
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Cites
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
Going Deeper with Embedded FPGA Platform for Convolutional Neural Network
Qiu, J., Wang, J., Yao, S., Guo, K., Li, B., Zhou, E., Yu, J., Tang, T., Xu, N., Song, S., et al. (2016) · ACM/SIGDA International Symposium on Field-Programmable Gate Arrays (FPGA) · doi:10.1145/2847263.2847265 · Cited by 1,290
Large-scale deep unsupervised learning using graphics processors
Raina, R., Madhavan, A., & Ng, A. Y. (2009) · Proceedings of the 26th Annual International Conference on Machine Learning · doi:10.1145/1553374.1553486 · Cited by 742
DLAU: A Scalable Deep Learning Accelerator Unit on FPGA
Wang, C., Gong, L., Yu, Q., Li, X., Xie, Y., & Zhou, X. (2016) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2016.2587683 · Cited by 326
Using GPUs for machine learning algorithms
Steinkraus, D., Buck, I., & Simard, P. Y. (2005) · Eighth International Conference on Document Analysis and Recognition (ICDAR'05) · doi:10.1109/icdar.2005.251 · Cited by 251
An OpenCL™ Deep Learning Accelerator on Arria 10
Aydonat, U., O’Connell, S., Capalija, D., Ling, A. C., & Chiu, G. R. (2017) · ACM/SIGDA International Symposium on Field-Programmable Gate Arrays (FPGA) · doi:10.1145/3020078.3021738 · Cited by 229
Design space exploration of FPGA-based Deep Convolutional Neural Networks
Motamedi, M., Gysel, P., Akella, V., & Ghiasi, S. (2016) · Asia and South Pacific Design Automation Conference (ASP-DAC) · doi:10.1109/aspdac.2016.7428073 · Cited by 211
Implementing Decision Trees and Forests on a GPU
Sharp, T. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-88693-8_44 · Cited by 176
FPGA-GPU architecture for kernel SVM pedestrian detection
Bauer, S., Köhler, S., Doll, K., et al. (2010) · 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2010.5543772 · Cited by 109
A GPU-Outperforming FPGA Accelerator Architecture for Binary Convolutional Neural Networks
Li, Y., Liu, Z., Xu, K., Yu, H., & Ren, F. (2018) · ACM Journal on Emerging Technologies in Computing Systems · doi:10.1145/3154839 · Cited by 83
Research works printed but not linked: 2
- Bergstra, J., Bastien, F., Breuleux, O., Lamblin, P., Pascanu, R., Delalleau, O., Desjardins, G., Warde-Farley, D., Goodfellow, I., Bergeron, A., et al., 2011. Theano: Deep learning on gpus with python, in: NIPS 2011, BigLearning Workshop, Granada, Spain, Citeseer.
- Zhao, W., Fu, H., Luk, W., Yu, T., Wang, S., Feng, B., Ma, Y., Yang, G., 2016. F-CNN: An FPGA-based Framework for Training Convolutional Neural Networks, in: Proceedings of the IEEE International Conference on Application-Specific Systems, Architectures and Processors, pp. 107–114. · DOI
Cited by
Sorted by citations
Embedded Intelligence on FPGA: Survey, Applications and Challenges
Seng, K. P., Lee, P. J., & Ang, L. M. (2021) · Electronics · doi:10.3390/electronics10080895 · Cited by 105
“Based on the research, the FPGA Arria 10 speed is six times faster than GPU and 44 times faster than CPU in the inferencing process [83].”
Method
Metadata
- Authors
- Liu, X., Ounifi, H. A., Gherbi, A., Lemieux, Y., & Li, W.
- Affiliations
- École de Technologie Supérieure (CA)
- Ericsson (Canada) (CA)
- Corresponding author
- Liu, Xu
- Ounifi, Hibat Allah
- Gherbi, Abdelouahed
- Lemieux, Yves
- Li, Wubin
- Venue
- Procedia Computer Science
- Publisher
- Elsevier BV
- Published
- 2018
- Volume
- 141
- Pages
- 104-111
- ISSN
- 1877-0509 (ISSN-L)
- OpenAlex
- W2900331569
- Cited by
- 26
- References
- 13
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 5 · 2023: 2 · 2022: 1 · 2021: 5 · 2020: 6 · 2019: 5
- FWCI
- 3.44
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Procedia Computer Science (publishedVersion, cc-by-nc-nd)
- Espace ÉTS (ETS) (acceptedVersion)
- Funders
- Natural Sciences and Engineering Research Council of Canada
- Topics
- Physical Sciences › Computer Science › Hardware and Architecture › Embedded Systems Design Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Embedded Systems and FPGA Applications
- Keywords
- machine learning acceleration, GPU training, LeNet-5, high-density computing, inference acceleration
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