Adaptive interfaces based on FPGA implemented artificial neural network [articol]

Abstract

The goal of this work is to build smart interfaces with learning and adaptive capability. The key element of the learning and adaptive behavior are artificial neural network (ANN) blocks, implemented in FPGA using the System Generator tool for Simulink developed by Xilinx Inc. This tool allow the easy generation of hardware Description Language (HDL) code from a system representation in Simulink. This VHDL design can then be synthesized for implementation in the Xilinx family of FPGA devices. The off-chip learning task is performed using Matlab and the ANN’s weights are transferred automatically from Matlab workspace to weights memory.