Autors: Dondon, P., Carvalho, J., Gardere, R., Lahalle, P., Tsenov, G. T., Mladenov, V. M.
Title: Implementation of a feed-forward Artificial Neural Network in VHDL on FPGA
Keywords: FPGA implementation, neural networks, nonlinear systems, VHD

Abstract: Describing an Artificial Neural Network (ANN) using VHDL allows a further implementation of such a system on FPGA. Indeed, the principal point of using FPGA for ANNs is flexibility that gives it an advantage toward other systems like ASICS which are entirely dedicated to one unique architecture and allowance to parallel programming, which is inherent to ANN calculation system and one of their advantages. Usually FPGAs do not have unlimited logical resources integrated in a single package and this limitation forcesrequirement for optimizations for the design in order to have the best efficiency in terms of speed and resource consumption. This paper deals with the VHDL designing problems which can be encountered when trying to describe and implement such ANNs on FPGAs.

References

    Issue

    12th Symposium on Neural Network Applications in Electrical Engineering, NEUREL 2014 - Proceedings, pp. 37-40, 2014, Serbia, Institute of Electrical and Electronics Engineers Inc., DOI 10.1109/NEUREL.2014.7011454

    Цитирания (Citation/s):
    1. Sarvan, C., Gunduzalp, M. Implementation of ANN Training Module on Field Programmable Gate Arrays, Proceedings - 2019 Innovations in Intelligent Systems and Applications Conference, ASYU 2019 8946350 - 2019 - в издания, индексирани в Scopus или Web of Science
    2. Khalil, K., Eldash, O., Dey, B., Kumar, A., Bayoumi, M. A Novel Reconfigurable Hardware Architecture of Neural Network, Midwest Symposium on Circuits and Systems 2019-August,8884809, pp. 618-621 - 2019 - в издания, индексирани в Scopus или Web of Science
    3. Wibowo, F.W. An Analysis of FPGA Hardware Platform Based Artificial Neural Network, Journal of Physics: Conference Series 1201(1),012009 - 2019 - в издания, индексирани в Scopus или Web of Science
    4. Yi, Q. A hardware implementation of SOM neural network algorithm, Proceedings - 2018 International Conference on Sensor Networks and Signal Processing, SNSP 2018 8615979, pp. 508-511 - 2019 - в издания, индексирани в Scopus или Web of Science
    5. Chhabra, A., Dhanoa, J. A Design Approach for Mac Unit Using Vedic Multiplier., 2020 5th IEEE International Conference on Recent Advances and Innovations in Engineering, ICRAIE 2020 – Proceeding, IEEE, DOI: 10.1109/ICRAIE51050.2020.9358368 - 2020 - в издания, индексирани в Scopus или Web of Science
    6. Guojian, X., Meihua, Z. Analysis of electric vehicle purchase behavior based on FPGA system and neural network., Microprocessors and Microsystems, (Article in press), 2020, Article number 103361, Elsevier, DOI: 10.1016/j.micpro.2020.103361, pp. 1-7. - 2020 - в издания, индексирани в Scopus или Web of Science
    7. Yang W.,Gao Y.,Zhai F., Simulation of sports action picture recognition based on FPGA and convolutional neural network, Microprocessors and Microsystems, Volume 80, February 2021 Article number 103593, DOI 10.1016/j.micpro.2020.103593 - 2021 - в издания, индексирани в Scopus или Web of Science
    8. Schmitz, Jesse, and Lei Zhang. "FPGA Hardware Implementation and Optimization for Neural Network based Chaotic System Design." In Proceedings of the 9th International Symposium on Highly-Efficient Accelerators and Reconfigurable Technologies, pp. 1-6. 2018. - 2018 - в издания, индексирани в Scopus или Web of Science
    9. Ann, Lee Yee, Phaklen Ehkan, and M. Y. Mashor. "Possibility of hybrid multilayered perceptron neural network realisation on FPGA and its challenges." In Advanced Computer and Communication Engineering Technology, pp. 1051-1061. Springer, Cham, 2016. - 2016 - в издания, индексирани в Scopus или Web of Science
    10. Conejo, Elian, Jean-Pierre Frangi, and Gilles De Rosny. "Neural network implementation for a reversal procedure for water and dry matter estimation on plant leaves using selected LED wavelengths." Applied optics 54, no. 17 (2015): 5453-5460. - 2015 - в издания, индексирани в Scopus или Web of Science
    11. Schuman, Catherine D., Thomas E. Potok, Robert M. Patton, J. Douglas Birdwell, Mark E. Dean, Garrett S. Rose, and James S. Plank. "A survey of neuromorphic computing and neural networks in hardware." arXiv preprint arXiv:1705.06963 (2017). - 2017 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
    12. Martínez‐Prado, M.A., Rodríguez‐Reséndiz, J., Gómez‐Loenzo, R.A., Camarillo‐Gómez, K.A. and Herrera‐Ruiz, G., 2019. Short informative title: Towards a new tendency in embedded systems in mechatronics for the engineering curricula. Computer Applications in Engineering Education, 27(3), pp.603-614. - 2019 - в издания, индексирани в Scopus или Web of Science
    13. Purnomo, Dwi, Machmud Roby Alhamidi, Ari Wibisono, and Muhammad Iqbal Tawakal. "Investigation of flip-flop performance on different type and architecture in shift register with parallel load applications." Jurnal Ilmu Komputer dan Informasi 8, no. 2 (2015): 83-91. - 2015 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
    14. Shirke, H. and Vijapur, N., 2017. FPGA Implementation of Glaucoma Detection Using Neural Networks. International Research Journal of Engineering and Technology, 4. - 2017 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
    15. Miers, Eric James. "Authenticating SiK Radios with RF Fingerprinting and Deep Neural Network Classifiers." PhD diss., Christopher Newport University, 2021. - 2021 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
    16. MING, LIM CHUN. "HARDWARE AND SOFTWARE IMPLEMENTATION OF ARTIFICIAL NEURAL NETWORK IN ALTERA DE1-SOC." (2017). - 2017 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
    17. Castro, Welbert, Milton Heinen, and Bruno Neves. "Arquitetura Adaptável para Execução de Redes Neurais Artificiais em Dispositivos FPGA." In Anais Estendidos do XX Simpósio em Sistemas Computacionais de Alto Desempenho, pp. 33-40. SBC, 2019. - 2019 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
    18. Nguyen, Anh Quang, and Dung Hoang Nguyen. "Tế bào Nơron nhân tạo có độ chính xác và tốc độ cao." VNU Journal of Science: Natural Sciences and Technology 33, no. 1 (2017). - 2017 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
    19. Nguyễn, Quang Anh, and Hoàng Dũng Nguyễn. "Tế bào Nơron nhân tạo có độ chính xác và tốc độ cao." (2017). - 2017 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
    20. COMPUTACIONALES, MAESTRO EN SISTEMAS. "IMPLEMENTACIÓN DE UNA RED NEURONAL EN FPGA PARA MODELADO DE SISTEMAS." PhD diss., INSTITUTO TECNOLÓGICO DE LA PAZ, 2021. - 2021 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
    21. Al-Musawi, Wisal Adnan, Wasan A. Wali, and Mohammed Abd Ali Al-Ibadi. "New artificial neural network design for Chua chaotic system prediction using FPGA hardware co-simulation." International Journal of Electrical & Computer Engineering (2088-8708) 12, no. 2 (2022). - 2022 - в издания, индексирани в Scopus или Web of Science
    22. Krishna Dutt R.V.S.aSend mail to Krishna Dutt R.V.S.,Ganesh R.b,Premchand P.c “Neural net implementation of steam properties on FPGA”, International Journal of Reconfigurable and Embedded SystemsOpen AccessVolume 10, Issue 3, Pages 186 – 194 November 2021 ISSN 20894864, DOI 10.11591/IJRES.V10.I3.PP186-194 - 2021 - в издания, индексирани в Scopus или Web of Science
    23. 21. Olayinka, T.C., Olayinka, A.S. and Nwankwo, W., 2021. EVOLVING FEED-FORWARD ARTIFICIAL NEURAL NETWORKS USING BINARY AND DENARY DATASET. SAU Science-Tech Journal, 6(1), pp.96-108. - 2021 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
    24. 75) León, R.C., Viscaya, J.A.M., Santillán, I. and Liera, M.A.C., Diseno de una Red Neuronal en FPGA para el Modelado de Sistemas Mecatronicos. ISBN: 978-607-98174-6-6, pp. 94 – 103, (Google Scholar) - 2021 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
    25. 76) Venkateswara Reddy, K. and Balaji, N., 2021, June. VLSI Implementation of the Low Power Neuromorphic Spiking Neural Network with Machine Learning Approach. In International Conference on Soft Computing and Signal Processing (pp. 781-793). Springer, Singapore. (Google Scholar, Scopus) - 2021 - в издания, индексирани в Scopus или Web of Science
    26. P. P. Kinikar and L. Shrinivasan, "Efficient Verilog implementation of Neural Networks for Handwritten Character Recognition," 2022 International Conference on Industry 4.0 Technology (I4Tech), 2022, pp. 1-5, doi: 10.1109/I4Tech55392.2022.9952365. (Scopus, Google Scholar) - 2022 - в издания, индексирани в Scopus или Web of Science
    27. Al-Musawi, W.A., Al-Ibadi, M.A.A. and Wali, W.A., 2023. Artificial intelligence techniques for encrypt images based on the chaotic system implemented on field-programmable gate array. IAES International Journal of Artificial Intelligence, vol. 12, issue (1), p.347., ISSN 20894872, DOI 10.11591/ijai.v12.i1. pp.347-356 (Scopus, Google Scholar) SJR 0.35 - 2023 - в издания, индексирани в Scopus или Web of Science
    28. Kumari, B.A., Kulkarni, S.P. and Sinchana, C.G., 2023. FPGA Implementation of Neural Nets. International Journal of Electronics and Telecommunications, VOL. 69, NO. 3, pp. 599-604, DOI: 10.24425/ijet.2023.146513. (Web of Science, Google Scholar) IF 0.7 - 2023 - в издания, индексирани в Scopus или Web of Science
    29. Khalil, K., Dey, B. and Bayoumi, M., 2023. S2RNN: Self-Supervised Reconfigurable Neural Network Hardware Accelerator for Machine Learning Applications. TechRxiv, vol. 4, pp. 1-13, https://doi.org/10.36227/techrxiv.22728788.v1 (Google Scholar) - 2023 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
    30. Sadeghikhah, K., Zhang, L. and Paranjape, R., 2023, September. An Efficient VHDL Implementation of two Artificial Neural Networks on Zynq-7000 FPGA. In 2023 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) (pp. 371-376). IEEE. ISSN 08407789, ISBN 979-835032397-9, DOI 10.1109/CCECE58730.2023.10288905 (Web of Science, Scopus, Google Scholar) - 2023 - в издания, индексирани в Scopus или Web of Science

    Вид: публикация в международен форум, публикация в реферирано издание, индексирана в Scopus и Web of Science