Hardware and software implementation of artificial neural network in altera de1-soc
Loading...
Date
2017-06
Authors
Lim, Chun Ming
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
Artificial neural network (ANN) has been widely used in many applications and has been
started to be implemented in embedded system. Recently new platform like Altera DE1-
SOC that contains both processor and FPGA had been introduced. When using this type
of platform, artificial neural network can be either implemented in processor using
software implementation or in FPGA using hardware implementation. Analysis should
be done to see whether processor or FPGA is a better choice for the ANN. In this project,
framework for implementation of ANN in processor and FPGA of Altera DE1-SOC has
been developed and the efficiency of implementation of ANN in processor and FPGA in
terms of accuracy, execution time and resources utilization has been studied. Several
multilayer perceptron (MLP) models with different number of inputs, number of hidden
neurons and types of activation function have first been trained in MATLAB and after
that, these trained models have been implemented in both processor and FPGA of Altera
DE1-SOC. Experiments have been carried out to test and measure the performance of
these MLP models in processor and FPGA. After comparing output result with ANN that
run in MATLAB and computing the mean squared error (MSE), results show that the
ANN in processor has 100% accuracy and ANN in FPGA has minimum MSE of 7.3 x
10-6. While ANN in FPGA is 20 times faster than ANN in processor. Therefore, if
accuracy is main priority and execution time is not so important in a system, ANN is
suggested to be implemented in processor. However, if execution time of ANN must be
fast like less than microsecond in a system, ANN is suggested to be implemented in FPGA.