Training artificial neural networks by pso to perform digital circuits using xilinx FPGA

Abstract EN

One of the major constraints on hardware implementations of Artificial Neural Networks (ANNs) is the amount of circuitry required to perform the multiplication process of each input by its corresponding weight and there subsequent addition.

Field Programmable Gate Array (FPGA) is a suitable hardware IC for Neural Network (NN) implementation as it preserves the parallel architecture of the neurons in a layer and offers flexibility in reconfiguration and cost issues.

In this paper the adaption of the ANN weights is proposed using Particle Swarm Optimization (PSO) as a mechanism to improve the performance of ANN and also for the reduction in the ANN hardware.

For this purpose we modified the MATLAB PSO toolbox to be suitable for the taken application.

In the proposed design training is done off chip then the fully trained design is download into the chip, in this way less circuitry is required.

This paper executes four bit Arithmetic Logic Unit (ALU) implemented using Xilinx schematic design entry tools as an example for the implementation of digital circuits using ANN trained by PSO algorithm.