FPGA implementation of an integrate-and-fire LEGION model for image segmentation

Abstract. Despite several previous studies, little progress has been made in building successful neural systems for image segmentation in digital hardware. Spiking neural networks offer an opportunity to develop models of visual perception without any complex structure based on multiple neural maps. Such models use elementary asynchronous computations that have motivated several implementations on analog devices, whereas digital implementations appear as quite unable to handle large spiking neural networks, for lack of density. In this work, we consider a model of integrate-and-fire neurons organized according to the standard LEGION architecture to segment grey-level images. Taking advantage of the local and distributed structure of the model, a massively distributed implementation on FPGA using pipelined serial computations is developed. Results show that digital and flexible solutions may efficiently handle large networks of spiking neurons.

• Type: Conference
• Year: 2006
• Where: ESANN
• Authors: Bernard Girau, Cesar Torres-Huitzil
• Keywords: ESANN 2006, ESANN 2007, Multiple Neural Maps, Spiking Neural Networks, Successful Neural Systems