IEEE Trans Neural Netw Learn Syst - A Spiking Neural Simulator Integrating Event-Driven and Time-Driven Computation Schemes Using Parallel CPU-GPU Co-Processing: A Case Study.

Tópicos

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Resumo

Time-driven simulation methods in traditional CPU architectures perform well and precisely when simulating small-scale spiking neural networks. Nevertheless, they still have drawbacks when simulating large-scale systems. Conversely, event-driven simulation methods in CPUs and time-driven simulation methods in graphic processing units (GPUs) can outperform CPU time-driven methods under certain conditions. With this performance improvement in mind, we have developed an event-and-time-driven spiking neural network simulator suitable for a hybrid CPU-GPU platform. Our neural simulator is able to efficiently simulate bio-inspired spiking neural networks consisting of different neural models, which can be distributed heterogeneously in both small layers and large layers or subsystems. For the sake of efficiency, the low-activity parts of the neural network can be simulated in CPU using event-driven methods while the high-activity subsystems can be simulated in either CPU (a few neurons) or GPU (thousands or millions of neurons) using time-driven methods. In this brief, we have undertaken a comparative study of these different simulation methods. For benchmarking the different simulation methods and platforms, we have used a cerebellar-inspired neural-network model consisting of a very dense granular layer and a Purkinje layer with a smaller number of cells (according to biological ratios). Thus, this cerebellar-like network includes a dense diverging neural layer (increasing the dimensionality of its internal representation and sparse coding) and a converging neural layer (integration) similar to many other biologically inspired and also artificial neural networks.

Resumo Limpo

timedriven simul method tradit cpu architectur perform well precis simul smallscal spike neural network nevertheless still drawback simul largescal system convers eventdriven simul method cpus timedriven simul method graphic process unit gpus can outperform cpu timedriven method certain condit perform improv mind develop eventandtimedriven spike neural network simul suitabl hybrid cpugpu platform neural simul abl effici simul bioinspir spike neural network consist differ neural model can distribut heterogen small layer larg layer subsystem sake effici lowact part neural network can simul cpu use eventdriven method highact subsystem can simul either cpu neuron gpu thousand million neuron use timedriven method brief undertaken compar studi differ simul method benchmark differ simul method platform use cerebellarinspir neuralnetwork model consist dens granular layer purkinj layer smaller number cell accord biolog ratio thus cerebellarlik network includ dens diverg neural layer increas dimension intern represent spars code converg neural layer integr similar mani biolog inspir also artifici neural network

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