A Methodology for implementation of the Execution Phase of Artificial Neural Networks in Digital Hardware Devices

Posted on March 12, 2009 by Brenda L.

Castro Peñaloza Ulises, Ibarra Esquer Jorge E., Flores Rios Brenda L. A Methodology for implementation of the Execution Phase of Artificial Neural Networks in Digital Hardware Devices. Electronics, Robotics and Automotive mechanics Conference (CERMA). 2008. ISBN 978-0-7695-3320-9/08.  Pp. 422-427. IEEE Conference Proceedings.

Abstract.
In this paper we describe a methodology for implementing the phase of execution of Artificial Neural Networks (ANN) in hardware devices. First, we show how the elements of a single neuron: multipliers, sum of products and transfer function are separated and constructed as VHDL entities. These entities are then interconnected to form a neuron that can be mapped to a hardware device. Using a similar approach, neurons are grouped in layers, which are then interconnected themselves to construct an Artificial Neural Network. The methodology is intended to lead a Neural Network designer through the steps required to take the design into a hardware device, starting with the results provided by a neurosimulator, obtaining the network parameters and translating them into a fully synthesizable design. A prototype of a Java-based ANN descriptor to VHDL translator is presented. In addition, the desired characteristics of neurosimulators are discussed and a comparison among different hardware platforms is shown.

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One Response to A Methodology for implementation of the Execution Phase of Artificial Neural Networks in Digital Hardware Devices

  1. Brenda L. says:
    August 24, 2011 at 6:42 pm

    Citado por 3:

    1. Suleiman, A. B. R., & Fatehi, T. A. H. (2007). Features Extraction Techniqes of EEG Signal for BCI Applications. Faculty of Computer and Information Engineering Department College of Electronics Engineering, University of Mosul, Iraq.


    2. AP Singh, P Chandra, Chandra Sekhar Rai. Fault Measuring Technique for Neural Hardware. Proceedings of the 2009 International Conference on Signals, Systems & Automation-2009. Pp: 329-333.

    3. Gustavo A. Alonso, Georges Istamboulie, Alfredo Ramírez-García, Thierry Noguer, Jean-Louis Marty, Roberto Muñoz. Artificial neural network implementation in single low-cost chip for the detection of insecticides by modeling of screen-printed enzymatic sensors response. Computers and Electronics in Agriculture. Vol. 74, Issue 2, November 2010, Pp: 223-229.
    doi:10.1016/j.compag.2010.08.003