NeuroFlow: A General Purpose Spiking Neural Network Simulation Platform using Customizable Processors.
NeuroFlow: A General Purpose Spiking Neural Network Simulation Platform using Customizable Processors.
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DOI：
10.3389/fnins.2015.00516
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发表时间：
2015
影响因子：
4.3
通讯作者：
Luk W
中科院分区：
文献类型：
--
作者：
Cheung K;Schultz SR;Luk W
NeuroFlow是一个可扩展的尖峰神经网络仿真平台，用于使用可定制硬件处理器（如现场可编程门阵列（FPGA））的现成高性能计算系统。与多核处理器和专用集成电路不同，NeuroFlow的处理器架构可以重新设计和重新配置，以适应特定的模拟，从而提供优化的性能，例如采用的并行度。编译过程支持使用PyNN（一种与模拟器无关的神经网络描述语言）来配置处理器。NeuroFlow支持许多常用的基于电流或电导的神经元模型，如整合和激发模型和Izhikevich模型，以及用于学习的尖峰定时依赖可塑性（STDP）规则。6-FPGA系统可以模拟多达600，000个神经元的网络，并可以实现400，000个神经元的实时性能。使用一个FPGA，NeuroFlow提供了高达33.6倍的8核处理器的速度，或基于GPU的平台的速度的2.83倍。具有高灵活性和高吞吐量，NeuroFlow为大规模神经网络仿真提供了一个可行的环境。
NeuroFlow is a scalable spiking neural network simulation platform for off-the-shelf high performance computing systems using customizable hardware processors such as Field-Programmable Gate Arrays (FPGAs). Unlike multi-core processors and application-specific integrated circuits, the processor architecture of NeuroFlow can be redesigned and reconfigured to suit a particular simulation to deliver optimized performance, such as the degree of parallelism to employ. The compilation process supports using PyNN, a simulator-independent neural network description language, to configure the processor. NeuroFlow supports a number of commonly used current or conductance based neuronal models such as integrate-and-fire and Izhikevich models, and the spike-timing-dependent plasticity (STDP) rule for learning. A 6-FPGA system can simulate a network of up to ~600,000 neurons and can achieve a real-time performance of 400,000 neurons. Using one FPGA, NeuroFlow delivers a speedup of up to 33.6 times the speed of an 8-core processor, or 2.83 times the speed of GPU-based platforms. With high flexibility and throughput, NeuroFlow provides a viable environment for large-scale neural network simulation.