An end user platform for implementing Artificial Neutron Networks on FPGA

The paper presents a novel neural design methodology based on the End User Programming concept. The proposed solution empowers end users, by means of abstracting the low-level hardware functionalities, to hardware implement Artificial Neural Networks (ANN) using field-programmable gate arrays (FPGA). The main outcomes include rapid ANN design and hardware implementation. A case study of an ANN as a pattern recognition module of an artificial olfaction system trained to identify four coffee brands is presented. An extended analysis has been carried out regarding the recognition rates versus data representation.