A Survey and Taxonomy of FPGA-based Deep Learning Accelerators
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
GPU platforms have been widely researched in the improvement of computer performance and power efficiency, as the research shows that GPU is too power-intensive a solution to compute the equivalent network weights compared to CPU and FPGA solutions.
“In addition, GPU platforms have been widely researched in the improvement of computer performance and power efficiency, as the research shows that GPU is too power-intensive a solution to compute the equivalent network weights compared to CPU and FPGA solutions.”
From Embedded Intelligence on FPGA: Survey, Applications and Challenges (2021) · cites this paper as [87]
Neural network models are also growing larger and larger, which is expressed in the algorithmic computation of significant numbers of model parameters.
“Neural network models are also growing larger and larger, which is expressed in the algorithmic computation of significant numbers of model parameters.”
From Embedded Intelligence on FPGA: Survey, Applications and Challenges (2021) · cites this paper as [87]
The comparison between CPU, GPU and FPGA had come into the picture in term of hardware resources, power consumption, design difficulty and accuracy.
“Therefore, the comparison between CPU, GPU and FPGA had come into the picture in term of hardware resources, power consumption, design difficulty and accuracy [87].”
From Embedded Intelligence on FPGA: Survey, Applications and Challenges (2021) · cites this paper as [87]
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 · see citing works
What is the best multi-stage architecture for object recognition?
Jarrett, K., Kavukcuoglu, K., Ranzato, M. A., & LeCun, Y. (2009) · 2009 IEEE 12th International Conference on Computer Vision · doi:10.1109/iccv.2009.5459469 · Cited by 2,153 · see citing works
Fixed Point Quantization of Deep Convolutional Networks
Lin, D. D., Talathi, S. S., & Annapureddy, V. S. (2015) · arXiv · doi:10.48550/arxiv.1511.06393 · Cited by 684 · see citing works
Designing efficient accelerator of depthwise separable convolutional neural network on FPGA
Ding, W., Huang, Z., Huang, Z., Tian, L., Wang, H., & Feng, S. (2019) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2018.12.008 · Cited by 85 · see citing works
Deep Convolutional Neural Network Inference with Floating-point Weights and Fixed-point Activations
Lai, L., Suda, N., & Chandra, V. (2017) · arXiv · doi:10.48550/arxiv.1703.03073 · Cited by 81 · see citing works
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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 · see citing works
“Therefore, the comparison between CPU, GPU and FPGA had come into the picture in term of hardware resources, power consumption, design difficulty and accuracy [87].”
Background- Retracted
- Authors
- Blaiech, A. G., Ben Khalifa, K., Valderrama, C., Fernandes, M. A. C., & Bedoui, M. H.
- Affiliations
- University of Monastir (TN)
- University of Mons (BE)
- Universidade Federal do Rio Grande do Norte (BR)
- Corresponding author
- Blaiech, Ahmed Ghazi
- Venue
- Journal of Systems Architecture
- Publisher
- Elsevier BV
- Published
- 2019-09
- Volume
- 98
- Pages
- 331-345
- ISSN
- 1383-7621 (ISSN-L)
- OpenAlex
- W2914219486
- Cited by
- 88
- References
- 5
- Citations by year
- 2026: 9 · 2025: 11 · 2024: 12 · 2023: 13 · 2022: 13 · 2021: 14 · 2020: 12 · 2019: 4
- FWCI
- 10.95
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Systems Architecture (publishedVersion)
- HAL (submittedVersion)
- ORBi UMONS (submittedVersion)
- 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
- FPGA, deep learning accelerators, hardware accelerators, reconfigurable hardware, neural network hardware, energy efficiency, parallel computing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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