NeuroSpike viewer: A graphical environment for efficient control, communication and display of large-scale real-time simulation of Spiking Neural Networks on embedded systems

Abstract

In recent years, the scientific community's interest for developing software or hardware tools capable of processing and displaying real-time simulations of large-scale Spiking Neural Networks (SNN) has been growing exponentially. Visualization of the simulations is crucial to observe and study the dynamics of the SNN, because these models involve high level of biological realism by encoding spatial-temporal information into action potentials or spikes like biological neurons do. Existing tools are based on supercomputers or customized neuromorphic architectures to compute large-scale SNN models. However, many of them are unsuitable to carry out real-time analysis of large scale SNN with minimal cost as large amount of complex data needs to be processed and displayed at the same time. This work intends to provide an efficient cost effective and power efficient emulation tool based on hardware-software hybrid to process, control, and display large-scale SNN network behavior in real time. The solution consists of a customized hardware architecture that computes the neural-synaptic parameters and a software environment that displays the same on the screen of a general purpose computer (Host). The data to be visualized is transferred to the host through high speed Ethernet link. The proposed solution tailors the use of the Ethernet link bandwidth to an optimum such that the customized neuromorphic architecture and the host computer duo can be used to visualize the dynamics of SNN network of any size in real-time. The hardware and software platforms are scalable in terms of number of neurons and synapses. This solution gives flexibility to the user to visualize and analyze SNN network of any size avoiding saturation of the high-speed serial Ethernet link while supporting the visualization of SNN dynamics with a good screen resolution.