SNAVA—A real-time multi-FPGA multi-model spiking neural network simulation architecture

Athul Sripad, Giovanny Sanchez, Mireya Zapata, Vito Pirrone, Taho Dorta, Salvatore Cambria, Albert Marti, Karthikeyan Krishnamourthy, Jordi Madrenas
2018 Neural Networks  
Spiking Neural Networks (SNN) for Versatile Applications (SNAVA) simulation platform is a scalable and programmable parallel architecture that supports realtime, large-scale, multi-model SNN computation. This parallel architecture is implemented in modern Field-Programmable Gate Arrays (FPGAs) devices to provide high performance execution and flexibility to support large-scale SNN models. Flexibility is defined in terms of programmability, which allows easy synapse and neuron implementation.
more » ... s has been achieved by using a specialpurpose Processing Elements (PEs) for computing SNNs, and analyzing and customizing the instruction set according to the processing needs to achieve maximum performance with minimum resources. The parallel architecture is interfaced with customized Graphical User Interfaces (GUIs) to configure the SNN's connectivity, to compile the neuron-synapse model and to monitor SNN's activity. Our contribution intends to provide a tool that allows to prototype SNNs faster than on CPU/GPU architectures but significantly cheaper than fabricating a customized neuromorphic chip. This could be potentially valuable to the computational neuroscience and neuromorphic engineering communities.
doi:10.1016/j.neunet.2017.09.011 pmid:29054036 fatcat:qair4swatvh3rlndbbxh76ootm