摘要:This paper describes the development of an Intellectual Property (IP) core in
VHDL able to implement a Multilayer Perceptron (MLP) artificial neural network
(ANN) topology with up to 2 hidden layers, 128 neurons, and 31 inputs per
neuron. Neural network models are usually developed by using programming
languages, such as Matlab®. However, their implementation in configurable logic
hardware requires the use of some other tools and hardware description
languages, such as as VHDL. For easy migration, a Matlab Graphical User
Interface (GUI) to automatically translate the ANN architecture to VHDL code has
been developed. In addition, the use of an activation function based on fuzzy
logic for the implementation of the MLP neural network simplifies the logic and
improves the results. The environment was tested using a typical prediction
problem, the Mackey-Glass series, where several ANN topologies were generated,
tested and implemented in an FPGA. Results show the excellent agreement between
the results provided by the software model and the hardware implementation.