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Efficient hardware implemenatations of feedforward neural networks using field programmable gate array
Other Title(s)
التنفيذ الكفء للمكونات المادية للشبكات العصبية ذات التغذية الأمامية باستخدام مصفوفة البوبات المنطقية القابلة للبرمجة
Joint Authors
Source
Journal of Engineering Sciences
Issue
Vol. 46, Issue 5 (30 Sep. 2018), pp.539-555, 17 p.
Publisher
Assiut University Faculty of Engineering
Publication Date
2018-09-30
Country of Publication
Egypt
No. of Pages
17
Main Subjects
Arts & Humanities (Multidisciplinary)
Topics
Abstract EN
Hardware implementation of Artificial Neural Network (ANNs) depends mainly on the efficient implementation of the activation functions.
Field Programmable Gate Array is the most appropriate tool for hardware implementation of ANNs.
In this paper we introduce FPGA-based hardware implementation of ANNs using five different activation functions.
These implemented NNs are described using Very High Speed Integrated Circuits Hardware Description Language (VHDL) and carried out by Digilent Basys 2 Spartan-3E FPGA platform from Xilinx.
The performances of the implemented NNs were investigated in terms of area efficient implementation, and correct prediction percentages for solving XOR, and Full-Adder problems.
American Psychological Association (APA)
Isa, Muhammad H.& Majdi, Marina. 2018. Efficient hardware implemenatations of feedforward neural networks using field programmable gate array. Journal of Engineering Sciences،Vol. 46, no. 5, pp.539-555.
https://search.emarefa.net/detail/BIM-912227
Modern Language Association (MLA)
Isa, Muhammad H.& Majdi, Marina. Efficient hardware implemenatations of feedforward neural networks using field programmable gate array. Journal of Engineering Sciences Vol. 46, no. 5 (Sep. 2018), pp.539-555.
https://search.emarefa.net/detail/BIM-912227
American Medical Association (AMA)
Isa, Muhammad H.& Majdi, Marina. Efficient hardware implemenatations of feedforward neural networks using field programmable gate array. Journal of Engineering Sciences. 2018. Vol. 46, no. 5, pp.539-555.
https://search.emarefa.net/detail/BIM-912227
Data Type
Journal Articles
Language
English
Notes
Record ID
BIM-912227