Boosting Convolutional Neural Networks Performance Based on FPGA Accelerator
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FPGA is widely utilized in various tasks including deep learning.
“FPGA is wildly utilized in various tasks including deep learning [199, 247, 331–334].”
From Review of deep learning: concepts, CNN architectures, challenges, applications, future directions (2021) · cites this paper as [199]
The Abstract and Introduction describe several FPGA-based accelerators for deep CNNs, supporting FPGA use in deep learning. The paper does not establish the broader claim that FPGAs are widely used across various tasks beyond that area; the Introduction’s examples of speech and vision applications describe CNNs, not FPGA use in each application. Page numbers are unavailable in our copy. (LLM, conf=0.82)
Experimental — accuracy not yet measuredField programmable gate array technology is an emerging hardware technology developed in recent years.
“Field programmable gate array (FPGA) technology is an emerging hardware technology developed in recent years [21].”
From Face Recognition Based on Deep Learning and FPGA for Ethnicity Identification (2022) · cites this paper as [21]
The Introduction (page not provided) describes recent developments in FPGA technology and growing use of FPGA-based CNN accelerators. It does not say that FPGA technology itself is emerging or was developed in recent years. (LLM, conf=0.82)
Experimental — accuracy not yet measured
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Self-citation
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Sorted by citations
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Self-citationReview of the State of the Art of Deep Learning for Plant Diseases: A Broad Analysis and Discussion
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Self-citationFace Recognition Based on Deep Learning and FPGA for Ethnicity Identification
AlBdairi, A. J. A., Xiao, Z., Alkhayyat, A., Humaidi, A. J., Fadhel, M. A., Taher, B. H., Alzubaidi, L., Santamaría, J., & Al-Shamma, O. (2022) · Applied Sciences · doi:10.3390/app12052605 · Cited by 54 · see citing works
Self-citationReal-Time Sickle Cell Anemia Diagnosis Based Hardware Accelerator
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Self-citation
- Authors
- Al-Shamma, O., Fadhel, M. A., Hameed, R. A., Alzubaidi, L., & Zhang, J.
- Affiliations
- University of Information Technology and Communications (IQ)
- University of Baghdad (IQ)
- Queensland University of Technology (AU)
- Corresponding author
- Fadhel, Mohammed Abdulraheem
- Venue
- Advances in Intelligent Systems and Computing
- Publisher
- Springer International Publishing
- Published
- 2019-04-12
- Pages
- 509-517
- ISSN
- 2194-5357 (ISSN-L)
- 2194-5365
- OpenAlex
- W2946390442
- Cited by
- 27
- References
- 23
- Citations by year
- 2024: 4 · 2023: 1 · 2022: 1 · 2021: 12 · 2020: 7 · 2019: 2
- FWCI
- 1.77
- Type
- conference paper
- Language
- en
- Open access
- green
- rdcu.be
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Advances in intelligent systems and computing (publishedVersion)
- QUT ePrints (Queensland University of Technology) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Hardware and Architecture › Embedded Systems Design Techniques
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Embedded Systems and FPGA Applications
- Keywords
- convolutional neural networks, FPGA accelerator, VGG16, hardware acceleration, fixed-point arithmetic, floating-point to fixed-point conversion, computation time reduction, image recognition, parallel computing
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