Title
SNAVA-A real-time multi-FPGA multi-model spiking neural network simulation architecture.
Abstract
Spiking Neural Networks (SNN) for Versatile Applications (SNAVA) simulation platform is a scalable and programmable parallel architecture that supports real-time, 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. This has been achieved by using a special-purpose 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.
Year
DOI
Venue
2018
10.1016/j.neunet.2017.09.011
Neural Networks
Keywords
Field
DocType
Digital neural simulation,FPGA,SNNs,Neuromorphic systems
Computer architecture,Instruction set,Computer science,Field-programmable gate array,Neuromorphic engineering,Chip,Compiler,Graphical user interface,Spiking neural network,Scalability,Embedded system
Journal
Volume
Issue
ISSN
97
1
0893-6080
Citations 
PageRank 
References 
1
0.35
16
Authors
9