• 主题
高级搜索  >
历史搜索 清空历史
首页> 美国卫生研究院文献>Frontiers in Neuroscience >Performance Comparison of the Digital Neuromorphic Hardware SpiNNaker and the Neural Network Simulation Software NEST for a Full-Scale Cortical Microcircuit Model
【2h】

Performance Comparison of the Digital Neuromorphic Hardware SpiNNaker and the Neural Network Simulation Software NEST for a Full-Scale Cortical Microcircuit Model

机译：数字神经形态硬件SpiNNaker和神经网络仿真软件NEST在全比例皮质微电路模型中的性能比较

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务，本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文，但由于OA文献来源多样且变更频繁，仍可能出现获取不到、文献不完整或与标题不符等情况，如果获取不到我们将提供退款服务。请知悉。
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The digital neuromorphic hardware SpiNNaker has been developed with the aim of enabling large-scale neural network simulations in real time and with low power consumption. Real-time performance is achieved with 1 ms integration time steps, and thus applies to neural networks for which faster time scales of the dynamics can be neglected. By slowing down the simulation, shorter integration time steps and hence faster time scales, which are often biologically relevant, can be incorporated. We here describe the first full-scale simulations of a cortical microcircuit with biological time scales on SpiNNaker. Since about half the synapses onto the neurons arise within the microcircuit, larger cortical circuits have only moderately more synapses per neuron. Therefore, the full-scale microcircuit paves the way for simulating cortical circuits of arbitrary size. With approximately 80, 000 neurons and 0.3 billion synapses, this model is the largest simulated on SpiNNaker to date. The scale-up is enabled by recent developments in the SpiNNaker software stack that allow simulations to be spread across multiple boards. Comparison with simulations using the NEST software on a high-performance cluster shows that both simulators can reach a similar accuracy, despite the fixed-point arithmetic of SpiNNaker, demonstrating the usability of SpiNNaker for computational neuroscience applications with biological time scales and large network size. The runtime and power consumption are also assessed for both simulators on the example of the cortical microcircuit model. To obtain an accuracy similar to that of NEST with 0.1 ms time steps, SpiNNaker requires a slowdown factor of around 20 compared to real time. The runtime for NEST saturates around 3 times real time using hybrid parallelization with MPI and multi-threading. However, achieving this runtime comes at the cost of increased power and energy consumption. The lowest total energy consumption for NEST is reached at around 144 parallel threads and 4.6 times slowdown. At this setting, NEST and SpiNNaker have a comparable energy consumption per synaptic event. Our results widen the application domain of SpiNNaker and help guide its development, showing that further optimizations such as synapse-centric network representation are necessary to enable real-time simulation of large biological neural networks.
机译：数字神经形态硬件SpiNNaker的开发旨在实现实时，低功耗的大规模神经网络仿真。实时性能以1 ms的积分时间步长实现，因此适用于可以忽略动力学更快的时间尺度的神经网络。通过减慢仿真速度，可以整合更短的积分时间步长，从而获得更快的时间标度，而这通常是生物学相关的。我们在这里描述了在SpiNNaker上具有生物时间标度的皮质微电路的第一个全面模拟。由于大约有一半的神经元突触出现在微电路内，因此较大的皮层回路每个神经元的突触仅适中。因此，满量程的微电路为模拟任意大小的皮层电路铺平了道路。该模型具有约80,000个神经元和3亿个突触，是SpiNNaker迄今为止最大的仿真模型。 SpiNNaker软件堆栈的最新开发实现了放大，从而使仿真可以分布在多个板上。与使用高性能集群上的NEST软件进行的仿真比较表明，尽管SpiNNaker具有定点算法，这两种仿真器仍可以达到相似的精度，这证明了SpiNNaker在具有生物学时间尺度和较大网络规模的计算神经科学应用中的可用性。还以皮质微电路模型为例，评估了两个模拟器的运行时间和功耗。为了以0.1 ms的时间步长获得与NEST相似的精度，与实时相比，SpiNNaker需要的减速系数约为20。使用带有MPI和多线程的混合并行化功能，NEST的运行时间可以达到3倍实时饱和。但是，实现此运行时间的代价是增加了功率和能耗。 NEST的最低总能耗在144个并行线程处达到4.6倍。在此设置下，NEST和SpiNNaker在每个突触事件中具有可比的能耗。我们的结果拓宽了SpiNNaker的应用领域，并有助于指导其发展，显示出进一步的优化（例如以突触为中心的网络表示）是实现大型生物神经网络实时仿真所必需的。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号