Design and implementation of two feed forward neural network models using FPGAs schematic editor

Abstract EN

The use Artificial Neural Networks (ANN) can be a form of Artificial Intelligence (AI).

The feed forward neural network has a wide application area such as pattern recognition, image compression, and classification problem.

Two models of a feed forward neural network are proposed and implemented using the schematic editor of the Xilinx FPGA foundation series 2.1 i.

Model-1 consists of two layers and specializes in solving a linear problem.

Model-2 is a modified copy from Model-1 and consists of three layers and it’s responsible for classifying the non-linear problems.

Each model is designed and implemented in five stages without using the finite slate machine.

The flexibility, low costly, and real-time operation are the main features of the proposed design take in considered.

Model-1 execution time is 2.935^s and model-2 execution time is 2.96[is, while the costs of two models are 1927 CLBs and 2017 CLBs respectively.

These features compare extremely well with other existing designs with good advantages.