An End-User Platform for FPGA-Based Design and Rapid Prototyping of Feedforward Artificial Neural Networks With On-Chip Backpropagation Learning
- Submitting institution
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Royal Holloway and Bedford New College
- Unit of assessment
- 12 - Engineering
- Output identifier
- 34867346
- Type
- D - Journal article
- DOI
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10.1109/TII.2016.2555936
- Title of journal
- IEEE Transactions on Industrial Informatics
- Article number
- -
- First page
- 1124
- Volume
- 12
- Issue
- 3
- ISSN
- 1551-3203
- Open access status
- Out of scope for open access requirements
- Month of publication
- April
- Year of publication
- 2016
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
1
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- According to the IEEE Xplore and Web of Science search engines (keywords used: EUP, FPGA, ANN), the paper is the only one to apply the end-user programming (EUP) concepts when designing FPGA implementable neural networks. The paper findings have helped in creating a reliable environment that allows the non-hardware engineers to design FPGA implementable ANNs.
The research methodology constitutes a novel approach and its findings set a reference for researchers who seek not only the development of a hardware abstraction layer for FPGA ready NNs design but the embedding of the human interaction factor in the design process as well.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
