An End-User Platform for FPGA-Based Design and Rapid Prototyping of Feedforward Artificial Neural Networks With On-Chip Backpropagation Learning

The hardware implementation of an artificial neural network (ANN) using field-programmable gate arrays (FPGAs) is a research field that has attracted much interest and attention. With the developments made, the programmer is now forced to face various challenges, such as the need to master various complex hardware–software development platforms, hardware description languages, and advanced ANN knowledge. Moreover, such an implementation is very time consuming. To address these challenges, this paper presents a novel neural design methodology using a holistic modeling approach. Based on the end-user programming concept, the presented solution empowers end users by means of abstracting the low-level hardware functionalities, streamlining the FPGA design process and supporting rapid ANN prototyping. A case study of an ANN as a pattern recognition module of an artificial olfaction system trained to identify four coffee brands is presented. The recognition rate versus training data features and data representation was analyzed extensively.