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更大的GPU加速的大脑模拟与程序连接

James C Knight¹, Thomas Nowotny²

¹Centre for Computational Neuroscience and Robotics, School of Engineering and Informatics, University of Sussex, Brighton, UK. J.C.Knight@sussex.ac.uk.

Nature computational science

|January 13, 2024

概括

模拟大脑模型需要大量的记忆力. 一种新的程序连接方法可以在飞行中生成神经连接,从而在单个GPU上实现大规模的大脑模拟.

科学领域:

计算神经科学是一种神经科学.
神经科学是一个神经科学.

背景情况:

大规模的尖端神经网络模型对于理解大脑功能至关重要.
以前的模拟方法需要大量的内存用于突触连接,需要高性能计算系统.

研究的目的:

为大型尖端神经网络模型引入一种新的,记忆效率高的模拟方法.
为了使复杂的大脑模拟更容易获得研究人员.

主要方法:

开发了一种"程序连接"方法,在模拟过程中动态生成连接和突触权重.
为图形处理单元 (GPU) 和GPU代码生成实施了优化.

主要成果:

在单个GPU上成功模拟了一个大型的视觉皮层模型 (4.13 × 10^6个神经元,24.2 × 10^9个突触).
与传统的存储方法相比,显著减少了记忆.

结论:

程序连接为模拟大规模神经网络提供了可行和高效的替代方案.
这种方法通过利用常见的GPU硬件,提高了先进的大脑建模研究的可访问性.

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