Knight James C, Furber Steve B
Advanced Processor Technologies Group, School of Computer Science, University of Manchester Manchester, UK.
Front Neurosci. 2016 Sep 14;10:420. doi: 10.3389/fnins.2016.00420. eCollection 2016.
While the adult human brain has approximately 8.8 × 10(10) neurons, this number is dwarfed by its 1 × 10(15) synapses. From the point of view of neuromorphic engineering and neural simulation in general this makes the simulation of these synapses a particularly complex problem. SpiNNaker is a digital, neuromorphic architecture designed for simulating large-scale spiking neural networks at speeds close to biological real-time. Current solutions for simulating spiking neural networks on SpiNNaker are heavily inspired by work on distributed high-performance computing. However, while SpiNNaker shares many characteristics with such distributed systems, its component nodes have much more limited resources and, as the system lacks global synchronization, the computation performed on each node must complete within a fixed time step. We first analyze the performance of the current SpiNNaker neural simulation software and identify several problems that occur when it is used to simulate networks of the type often used to model the cortex which contain large numbers of sparsely connected synapses. We then present a new, more flexible approach for mapping the simulation of such networks to SpiNNaker which solves many of these problems. Finally we analyze the performance of our new approach using both benchmarks, designed to represent cortical connectivity, and larger, functional cortical models. In a benchmark network where neurons receive input from 8000 STDP synapses, our new approach allows 4× more neurons to be simulated on each SpiNNaker core than has been previously possible. We also demonstrate that the largest plastic neural network previously simulated on neuromorphic hardware can be run in real time using our new approach: double the speed that was previously achieved. Additionally this network contains two types of plastic synapse which previously had to be trained separately but, using our new approach, can be trained simultaneously.
虽然成年人类大脑大约有8.8×10¹⁰个神经元，但这个数量与它的1×10¹⁵个突触相比就显得微不足道了。从神经形态工程和一般神经模拟的角度来看，这使得对这些突触的模拟成为一个特别复杂的问题。SpiNNaker是一种数字神经形态架构，旨在以接近生物实时的速度模拟大规模脉冲神经网络。目前在SpiNNaker上模拟脉冲神经网络的解决方案在很大程度上受到分布式高性能计算工作的启发。然而，虽然SpiNNaker与这类分布式系统有许多共同特征，但其组件节点的资源要有限得多，而且由于系统缺乏全局同步，在每个节点上执行的计算必须在固定的时间步长内完成。我们首先分析了当前SpiNNaker神经模拟软件的性能，并确定了在使用它来模拟常用于建模包含大量稀疏连接突触的皮层的网络类型时出现的几个问题。然后，我们提出了一种新的、更灵活的方法，将此类网络的模拟映射到SpiNNaker上，从而解决了其中许多问题。最后，我们使用旨在表示皮层连接性的基准测试以及更大的功能性皮层模型来分析我们新方法的性能。在一个基准网络中，神经元从8000个STDP突触接收输入，我们的新方法允许在每个SpiNNaker核心上模拟的神经元数量比以前增加4倍。我们还证明，以前在神经形态硬件上模拟的最大的可塑性神经网络可以使用我们的新方法实时运行：速度是以前的两倍。此外，这个网络包含两种类型的可塑性突触，以前必须分别进行训练，但使用我们的新方法，可以同时进行训练。
