Computational efficacy of GPGPU-accelerated simulation for various neuron models
Computational efficacy of GPGPU-accelerated simulation for various neuron models
复制标题
GPGPU加速模拟各种神经元模型的计算效能
DOI：
10.1007/978-3-319-70139-4_81
复制
发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Y. Kashimori
中科院分区：
文献类型：
--
作者：
S. Okuno;K. Fujita;Y. Kashimori
为了理解大脑中感觉信息的处理机制，有必要模拟一个巨大的网络，该网络由模拟实际神经元的复杂神经元模型表示。然而，这样的模拟需要非常长的计算时间，不能以真实的时间尺度执行计算机模拟。为了解决计算时间的问题，我们重点研究了如何利用GPGPU来减少计算时间，为大量神经元的模拟提供了一种有效的方法。在本文中，我们开发了一个计算架构的GPGPU，通过它的神经元的计算并行执行。使用这种架构，我们表明，GPGPU方法显着减少了神经网络模拟的计算时间。我们还表明，模拟与单双浮点精度给小的显着差异的结果，独立的神经元模型使用。这些结果表明，单浮点精度的GPGPU计算可能是一个最有效的方法来模拟一个巨大的规模的神经网络。
To understand the processing mechanism of sensory information in the brain, it is necessary to simulate a huge size of network that is represented by a complicated neuron model imitating actual neurons. However, such a simulation requires a very long computation time, failing to perform computer simulation with a realistic time scale. In order to solve the problem of computation time, we focus on the reduction of computation time by GPGPU, providing an efficient method for simulation of huge number of neurons. In this paper, we develop a computational architecture of GPGPU, by which computation of neurons is performed in parallel. Using this architecture, we show that the GPGPU method significantly reduces the computation time of neural network simulation. We also show that the simulations with single and double float precision give little significant difference in the results, independently of the neuron models used. These results suggest that the GPGPU computation with single float precision could be a most efficient method for simulation of a huge size of neural network.