Hardware implementation of radial-basis neural networks with Gaussian activation functions on FPGA
Authors:
- Volodymyr Shymkovych,
- Sergii Telenyk,
- Petro Kravets
Abstract
This article introduces a method for realizing the Gaussian activation function of radial-basis (RBF) neural networks with their hardware implementation on field-programmable gaits area (FPGAs). The results of modeling of the Gaussian function on FPGA chips of different families have been presented. RBF neural networks of various topologies have been synthesized and investigated. The hardware component implemented by this algorithm is an RBF neural network with four neurons of the latent layer and one neuron with a sigmoid activation function on an FPGA using 16-bit numbers with a fixed point, which took 1193 logic matrix gate (LUTs—LookUpTable). Each hidden layer neuron of the RBF network is designed on an FPGA as a separate computing unit. The speed as a total delay of the combination scheme of the block RBF network was 101.579 ns. The implementation of the Gaussian activation functions of the hidden layer of the RBF network occupies 106 LUTs, and the speed of the Gaussian activation functions is 29.33 ns. The absolute error is ± 0.005. The Spartan 3 family of chips for modeling has been used to get these results. Modeling on chips of other series has been also introduced in the article. RBF neural networks of various topologies have been synthesized and investigated. Hardware implementation of RBF neural networks with such speed allows them to be used in real-time control systems for highspeed objects.
- Record ID
- CUTbf230fdbc68d4208b303fa728adef35e
- Publication categories
- ;
- Author
- Journal series
- Neural Computing & Applications, ISSN 0941-0643, e-ISSN 1433-3058
- Issue year
- 2021
- Vol
- 33
- No
- 15
- Pages
- 9467-9479
- Other elements of collation
- rys.; tab.; wykr.; Bibliografia (na s.) - 9478-9479; Bibliografia (liczba pozycji) - 53; Oznaczenie streszczenia - Abstr.; Data udostępnienia on-line - 2021-03-13; Numeracja w czasopiśmie - Vol. 33, Iss. 15
- Keywords in English
- approximation algorithm, RBF neural network, Gaussian activation functions, FPGA
- DOI
- DOI:10.1007/s00521-021-05706-3 Opening in a new tab
- URL
- https://link.springer.com/article/10.1007/s00521-021-05706-3 Opening in a new tab
- Language
- eng (en) English
- License
- Score (nominal)
- 100
- Additional fields
- Indeksowana w: Web of Science, Scopus
- Uniform Resource Identifier
- https://cris.pk.edu.pl/info/article/CUTbf230fdbc68d4208b303fa728adef35e/
- URN
urn:pkr-prod:CUTbf230fdbc68d4208b303fa728adef35e
* presented citation count is obtained through Internet information analysis, and it is close to the number calculated by the Publish or PerishOpening in a new tab system.