title: Event- and Time-Driven Techniques Using Parallel CPU-GPU Co-processing for Spiking Neural Networks.
abstract: Modeling and simulating the neural structures which make up our central neural system is instrumental for deciphering the computational neural cues beneath. Higher levels of biological plausibility usually impose higher levels of complexity in mathematical modeling, from neural to behavioral levels. This paper focuses on overcoming the simulation problems (accuracy and performance) derived from using higher levels of mathematical complexity at a neural level. This study proposes different techniques for simulating neural models that hold incremental levels of mathematical complexity: leaky integrate-and-fire (LIF), adaptive exponential integrate-and-fire (AdEx), and Hodgkin-Huxley (HH) neural models (ranged from low to high neural complexity). The studied techniques are classified into two main families depending on how the neural-model dynamic evaluation is computed: the event-driven or the time-driven families. Whilst event-driven techniques pre-compile and store the neural dynamics within look-up tables, time-driven techniques compute the neural dynamics iteratively during the simulation time. We propose two modifications for the event-driven family: a look-up table recombination to better cope with the incremental neural complexity together with a better handling of the synchronous input activity. Regarding the time-driven family, we propose a modification in computing the neural dynamics: the bi-fixed-step integration method. This method automatically adjusts the simulation step size to better cope with the stiffness of the neural model dynamics running in CPU platforms. One version of this method is also implemented for hybrid CPU-GPU platforms. Finally, we analyze how the performance and accuracy of these modifications evolve with increasing levels of neural complexity. We also demonstrate how the proposed modifications which constitute the main contribution of this study systematically outperform the traditional event- and time-driven techniques under increasing levels of neural complexity.
authors.affiliation: Department of Computer Architecture and Technology, Research Centre for Information and Communication Technologies, University of Granada Granada, Spain.
authors.forename: Francisco
authors.initials: F
authors.lastname: Naveros
authors.affiliation: Department of Computer Architecture and Technology, Research Centre for Information and Communication Technologies, University of Granada Granada, Spain.
authors.forename: Jesus A
authors.initials: JA
authors.lastname: Garrido
authors.affiliation: Department of Computer Architecture and Technology, Research Centre for Information and Communication Technologies, University of Granada Granada, Spain.
authors.forename: Richard R
authors.initials: RR
authors.lastname: Carrillo
authors.affiliation: Department of Computer Architecture and Technology, Research Centre for Information and Communication Technologies, University of Granada Granada, Spain.
authors.forename: Eduardo
authors.initials: E
authors.lastname: Ros
authors.affiliation: Vision Institute, Aging in Vision and Action LabParis, France; CNRS, INSERM, Pierre and Marie Curie UniversityParis, France.
authors.forename: Niceto R
authors.initials: NR
authors.lastname: Luque
keywords: CPU; GPU; bi-fixed-step integration methods; event- and time-driven techniques; look-up table; spiking neural models