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首页> 外文期刊>Neural Networks: The Official Journal of the International Neural Network Society >A configurable simulation environment for the efficient simulation of large-scale spiking neural networks on graphics processors.
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A configurable simulation environment for the efficient simulation of large-scale spiking neural networks on graphics processors.

机译：一个可配置的仿真环境，用于在图形处理器上高效地仿真大规模尖峰神经网络。

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摘要

Neural network simulators that take into account the spiking behavior of neurons are useful for studying brain mechanisms and for various neural engineering applications. Spiking Neural Network (SNN) simulators have been traditionally simulated on large-scale clusters, super-computers, or on dedicated hardware architectures. Alternatively, Compute Unified Device Architecture (CUDA) Graphics Processing Units (GPUs) can provide a low-cost, programmable, and high-performance computing platform for simulation of SNNs. In this paper we demonstrate an efficient, biologically realistic, large-scale SNN simulator that runs on a single GPU. The SNN model includes Izhikevich spiking neurons, detailed models of synaptic plasticity and variable axonal delay. We allow user-defined configuration of the GPU-SNN model by means of a high-level programming interface written in C++ but similar to the PyNN programming interface specification. PyNN is a common programming interface developed by the neuronal simulation community to allow a single script to run on various simulators. The GPU implementation (on NVIDIA GTX-280 with 1 GB of memory) is up to 26 times faster than a CPU version for the simulation of 100K neurons with 50 Million synaptic connections, firing at an average rate of 7 Hz. For simulation of 10 Million synaptic connections and 100K neurons, the GPU SNN model is only 1.5 times slower than real-time. Further, we present a collection of new techniques related to parallelism extraction, mapping of irregular communication, and network representation for effective simulation of SNNs on GPUs. The fidelity of the simulation results was validated on CPU simulations using firing rate, synaptic weight distribution, and inter-spike interval analysis. Our simulator is publicly available to the modeling community so that researchers will have easy access to large-scale SNN simulations.
机译：考虑到神经元尖峰行为的神经网络模拟器对于研究大脑机制和各种神经工程应用很有用。传统上，尖刺神经网络（SNN）模拟器是在大型集群，超级计算机或专用硬件体系结构上模拟的。另外，计算统一设备体系结构（CUDA）图形处理单元（GPU）可以提供一种低成本，可编程且高性能的计算平台来模拟SNN。在本文中，我们演示了在单个GPU上运行的高效，生物学上现实的大规模SNN模拟器。 SNN模型包括Izhikevich突刺神经元，突触可塑性和可变轴突延迟的详细模型。我们允许通过用C ++编写但与PyNN编程接口规范相似的高级编程接口来用户定义GPU-SNN模型的配置。 PyNN是神经元模拟社区开发的通用编程接口，允许单个脚本在各种模拟器上运行。 GPU的实现（在具有1 GB内存的NVIDIA GTX-280上）比CPU版本快26倍，用于模拟具有5000万个突触连接的100K神经元，平均触发频率为7 Hz。为了模拟1000万个突触连接和100K神经元，GPU SNN模型仅比实时模型慢1.5倍。此外，我们提出了一系列与并行性提取，不规则通信的映射和网络表示有关的新技术，以在GPU上有效地模拟SNN。使用射击频率，突触权重分布和尖峰间隔分析在CPU仿真中验证了仿真结果的保真度。我们的模拟器可供建模社区公开使用，因此研究人员将可以轻松访问大规模SNN模拟。

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