FPGA design and hardware implementation of a convolutional neural network for classification of saccadic eye movements

Abstract

The paper presents an efficient design and implementation of a convolutional neural network on an FPGA device. The aim is not only theoretical but also practical, since the solution will be used in a medical clinic dealing with SpinoCerebellar Ataxia type 2 as part of a larger project. Hence, the current work targets both high learning capabilities as well as portability. The former has been tackled through the apppointment a convolutional neural network while the latter is concerned with the hardware implementation of the complex network on a FPGA. The preliminary results encourage the further exploitation of the proposed solution.