PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Implementacja sieci neuronowej MLP FPNN w strukturze układu programowalnego FPGA

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
EN
Implementation of MLP FPNN neural network in FPGA programmable logic structure
Języki publikacji
PL
Abstrakty
PL
W artykule przedstawiono opis budowy i implementacji sieci neuronowej typu MLP FPNN w układzie FPGA. Przedstawiono budowę struktury oryginalnej sieci FPNN oraz budowę struktury dwóch sieci będących modyfikacjami oryginalnej sieci FPNN. Sieci neuronowe zostały zaimplementowane w układzie programowalnym. Przeprowadzono badania sprawdzające dokładność i czas realizacji obliczeń wykonywanych przez sieci neuronowe. Otrzymane wyniki potwierdzają zasadność implementacji zmodyfikowanej struktury sieci typu MLP FPNN.
EN
In the article a description of structure and implementation of MLP FPNN neural networks in FPGA’s was presented. Structure of the original FPNN net and its modifications were presented. Neural networks were implemented in programmable logic. Experiments were performed to verify the precision and speed of computations conducted by the neural networks. Results obtained prove the legitimacy of implementing the modified MLP FPNN structure.
Rocznik
Strony
217--223
Opis fizyczny
Bibliogr. 27 poz., rys., tab.
Twórcy
autor
autor
autor
  • Politechnika Warszawska, Instytut Sterowania i Elektroniki Przemysłowej, ul. Koszykowa 75, 00-662 Warszawa, L.Grzesiak@isep.pw.edu.pl
Bibliografia
  • [1] Maguire, L.P.; McGinnity, T.M.; Glackin, B.; Ghani, A.; Belatreche, A.; Harkin, J. Challenges for large-scale implementations of spiking neural networks on FPGAs, Neurocomputing, 71 (2007), n.1-3, 13–29
  • [2] Upegui, A.; Peña-Reyes, C. A.; Sanchez E. An FPGA platform for on-line topology exploration of spiking neural networks, Microprocessors and Microsystems, 29 (2005), n.5, 211–223
  • [3] LI, X.; Moussa, M.; Areibi, S. Arithmetic formats for implementing Artificial Neural Networks on FPGAs, Canadian J. of Electrical and Computer Engineering, 31 (2006), 31-40
  • [4] Gironés, R. G.; Palero, R.C.; Boluda, J. C.; Cortés, A. S. FPGA Implementation of a Pipelined On-Line Backpropagation, Journal of VLSI Signal Processing, 40 (2005), n.2, 189–213
  • [5] Dinu, A.; Cirstea M. A Digital Neural Network FPGA Direct Hardware Implementation Algorithm, IEEE International Symposium on Industrial Electronics, (2007), 2307-2312
  • [6] Ferreira, P.; Ribeiro, P.; Antunes, A.; Morgado, D. F. A high bit resolution FPGA implementation of a FNN with a new algorithm for the activation function, Neurocomputing, 71 (2007), n.1-3, 71–77
  • [7] Girau, B.; Khalifa, K.B. Fpga-Targeted Neural Architecture For Embedded Alertness Detection, 24th IASTED Int. Conf. on Artificial intelligence and applications, (2006), 199-204
  • [8] Krips, M.; Lammer t, T.; Kummer t A. FPGA Implementation of a Neural Network for a Real-Time Hand Tracking System, The First IEEE Int. Workshop on Electronic Design, Test and Applications (DELTA '02), (2002), 313-317
  • [9] Muthuramalingam, A.; Himavathi, S.; Srinivasan, E. Neural Network Implementation Using FPGA: Issues and Application, International Journal of Information Technology, 4 (2005), n.2, 86-92
  • [10] Nedjah, N.; De Macedo Mourelle, L. Reconfigurable hardware for neural networks: binary versus stochastic, Neural Comput & Applic, 16 (2007), n.3, 249–255
  • [11] Ortigosa, E.M.; Cañas, A.; Ros, E.; Ortigosa, P.M.; Mota, S.; Díaz, J. Hardware description of multi-layer perceptrons with different abstraction levels, Microprocessors and Microsystems, 30 (2006), n.7, 435–444
  • [12] Prieto, B.; De Lope, J.; Maravall, D. Reconfigurable Hardware Implementation of Neural Networks for Humanoid Locomotion, Lecture Notes in Computer Science, Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired Approach, 3562 (2005), 395-404
  • [13] Savran, A.; Ünsal, S. Hardware Implementation Of A Feedforward Neural Network Using FPGAs, ELECO'2003 3th International Conference On Electrical And Electronics Engineering Papers, (2003)
  • [14] Bajger, M.; Omondi, A. Low-error, High-speed Approximation of the Sigmoid Function for Large FPGA Implementations, Journal of Signal Processing Systems, 52 (2008), n.2, 137–151
  • [15] Himavathi, S.; Anitha, D.; Muthuramalingam, A. Feedforward Neural Network Implementation in FPGA Using Layer Multiplexing for Effective Resource Utilization, IEEE Transactions On Neural Networks, 18 (2007), n.3, 880-888
  • [16] Stepanova, M.; Valerie, F. L.; Lin, C.-L. A Hopfield Neural Classifier and Its FPGA Implementation for Identification of Symmetrically Structured DNA Motifs, Journal of VLSI Signal Processing, 48 (2007), n.3, 239–254
  • [17] Won, E. A hardware implementation of artificial neural networks using field programmable gate arrays, Nuclear Instruments and Methods in Physics Research, 581 (2007), n.3, 816–820
  • [18] Morgan D. Numerical Methods - Real-Time and Embedded Systems Programming, M&T Publishing, Inc, (1992), ISBN 1- 55851-232-2
  • [19] Omondi, A. R.; Rajapakse, J. C. FPGA Implementations of Neural Networks, Springer, 2006, ISBN-13: 978-0-387-28485-9
  • [20] Komzsik L., Approximation Techniques for Engineers, CRC Press, (2007), ISBN-13: 978-0-8493-9277-1
  • [21] Xilinx Corp., UG 681: ISE Design Suite Software Manuals and Help - PDF Collection, (2009)
  • [22] Pong P. Chu, RTL Hardware Design Using VHDL, Coding for Efficiency, Portability, and Scalability, John Wiley & Sons, Inc., (2006), ISBN-10: 0471720925
  • [23] Xilinx Corp., UG369: Virtex-6 FPGA DSP48E1 Slice - User Guide, (2008)
  • [24] Xilinx Corp., DS444: Block RAM (BRAM) Block (v1.00a), (2004)
  • [25] MathWorks Corp.: Neural Network Toolbox™ 6 User’s Guide, (2009)
  • [26] MathWorks Corp.: Statistics Toolbox™ 7 User’s Guide, (2009)
  • [27] Xilinx Corp., DS150: Virtex-6 Family Overview, (2009)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPOM-0030-0020
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.