Advertisement

FPGA Implementations of Neural Networks – A Survey of a Decade of Progress

  • Jihan Zhu
  • Peter Sutton
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2778)

Abstract

The ferst successful FPGA implementation [1] of artificial neural networks (ANNs) was published a little over a decade ago. It is timely to review the progress that has been made in this research area. This brief survey provides a taxonomy for classifying FPGA implementations of ANNs. Different implementation techniques and design issues are discussed. Future research trends are also presented.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Cox, C.E., Blanz, E.: GangLion - a fast field Programmable gate array implementation of a connectionist classifier. IEEE Journal of Solid-State Circuits 28(3), 288–299 (1992)CrossRefGoogle Scholar
  2. 2.
    Eldredge, J.G., Hutchings, B.L.: Density enhancement of a neural network using FPGAs and run-time reconfiguration. In: Proceedings of IEEE Workshop an Field-Programmable Custom Computing Machines, pp. 180–188 (1994)Google Scholar
  3. 3.
    James-Roxby, P., Blodget, B.A.: Adapting constant multipliers in a neural network implementation. In: Proceedings of IEEE Symposium an Field-Programmable Custom Computing Machines, pp. 335–336 (2000)Google Scholar
  4. 4.
    Zhu, J., Milne, G.J., Gunther, B.K.: Towards an FPGA based reconfigurable computing environment for neural network implementations. In: Proceedings of 9th International Conference an Artificial Neural Networks, vol.2, pp. 661-666 (1999)Google Scholar
  5. 5.
    Guccione, S.A., Gonzalez, M.: Classification and Performance ofreconfigurable architectures. In: Proceedings of the 5th International Workshop an Field-Programmable Logic and Applications, pp. 439–448. Springer, Berlin (1995)CrossRefGoogle Scholar
  6. 6.
    Perez-Uribe, A., Sanchez, E.: FPGA Implementation of an Adaptable-Size Neural Network. In: Proceedings of the Sixth International Conference an Artificial Neural Networks, pp. 382–388. Springer, Heidelberg (1996)Google Scholar
  7. 7.
    de Garis, H., et al.: Initial evolvability experiments an the CAM-brain machines (CBMs). In: Proceedings of the, Congress an Evolutionary Computation, vol.2, pp. 635-641 (2001)Google Scholar
  8. 8.
    Zhu, J., Milne, G.: Implementing Kak Neural Networks an a Reconfigurable Computing Pla form. In: Proceedings of the 10th International Workshop an Field Programmable Logic and Applications - Roadmap to Reconfigurable Computing, pp. 260–269. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  9. 9.
    Marchesi, M., et al.: Fast neural Ntworks without multipliers. IEEE Transactions an Neural Networks 4(1), 53–62 (1993)CrossRefGoogle Scholar
  10. 10.
    Nichols, K., Moussa, M., Areibi, S.: Feasibility of Floating-Point Arithmetic in FPGA based Artificial Neural Networks. In: Proceedings of the 15th International Conference an Computer Applications in Industry and Engineering, San Diego, Califomia (2002)Google Scholar
  11. 11.
    Reyneri, L.M.: Theoretical and implementation aspects ofpulse streams: an overview. In: Proceedings of the 7th International Conference an Microelectronics for Neural, Fuzzy and Bio-Inspired Systems, pp. 78–89 (1999)Google Scholar
  12. 12.
    Hikawa, H.: A new digital pulse-mode neuron with adjustable activation function. IEEE Transactions an Neural Networks 14(1045-9227), 236–242 (2003)CrossRefGoogle Scholar
  13. 13.
    Holt, J.L., Baker, T.E.: Back propagation simulations using limited precision calculations. In: Proceedings of International Joint Conference an Neural Networks, vol. 2, pp. 121–126 (1991)Google Scholar
  14. 14.
    Draghici, S.: On the capabilities of neural networks using limited precision weights. Neural Networks 15, 395–414 (2002)CrossRefGoogle Scholar
  15. 15.
    Wolf, D.F., Romero, R.A.F., Marques, E.: Using Embedded Processors in Hardware Models of Artificial Neural Networks. In: Proceedings of SBAI - Simpósio Brasileiro de Automao Inteligente, pp. 78–83 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Jihan Zhu
    • 1
  • Peter Sutton
    • 1
  1. 1.School of Information Technology and Electrical EngineeringThe University of QueenslandBrisbaneAustralia

Personalised recommendations