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FPGA implementation of a pulse density neural network with learning ability using simultaneous perturbation.

Y Maeda¹, T Tada

¹Dept. of Electr. Eng., Kansai Univ., Osaka, Japan.

IEEE Transactions on Neural Networks

|February 2, 2008

Summary

This study presents a hardware neural network (NN) using pulse density and simultaneous perturbation learning. This approach simplifies weight modification for electronic systems, enabling robust and efficient NN hardware realization.

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Area of Science:

* Computer Engineering and Artificial Intelligence.
* Focuses on hardware implementation of neural networks (NNs).

Background:

* Hardware realization of neural networks (NNs) is crucial for broader applications.
* Learning schemes for hardware NNs are of significant interest, with backpropagation being common but difficult to implement electronically.
* Pulse density NN systems offer robustness to noise and analog processing via digital circuits.

Purpose of the Study:

* To explore an alternative learning scheme for hardware neural networks.
* To demonstrate the feasibility of implementing a pulse density neural network (PDNN) on a field-programmable gate array (FPGA).
* To utilize the simultaneous perturbation method for efficient weight modification in hardware NNs.

Main Methods:

* Implementation of a pulse density neural network (PDNN) on a field-programmable gate array (FPGA).

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* Application of the simultaneous perturbation method as the learning scheme for the NN.

* Utilizing forward operations for weight modification, avoiding complex gradient calculations.

Main Results:

* Successful hardware realization of a pulse density neural network (PDNN) on an FPGA.
* Demonstration of the simultaneous perturbation method as a viable learning scheme for hardware NNs.
* Confirmation of the designed NN system's viability and operational capability through practical examples.

Conclusions:

* The simultaneous perturbation method is a practical and efficient learning scheme for hardware neural networks.
* FPGA implementation of pulse density NNs is achievable and effective.
* This approach offers a promising direction for developing robust and applicable hardware NN systems.