• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文OA文献 >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：用于全尺寸皮质微电路模型的数字神经门硬件纺丝机与神经网络仿真软件巢的性能比较

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：数字神经形状五金纺纱机已经通过实时实现大规模神经网络仿真和低功耗。使用1毫秒的集成时间步骤实现实时性能，因此适用于可以忽略动态的更快时间尺度的神经网络。通过减慢模拟，可以纳入较短的集成时间步骤，并且因此可以纳入更快的时间尺度，这通常是生物相关的。我们在这里描述了在纺纱机上的生物时间秤的皮质微电路的第一个全尺寸模拟。由于大约一半的突触在微电路内产生的突触，因此较大的皮质电路每神经元仅具有适度更多的突触。因此，全尺寸的微电路为模拟任意尺寸的皮质电路铺平道路。该模型与大约80,000个神经元和0.3亿个突触，是迄今为止旋转线上的最大模拟。 Spinnaker软件堆栈中最近的开发启用了缩放，允许模拟跨多个板分布。尽管Spinnaker的固定点算术，但是，尽管Spinnaker的定点算术，但是使用嵌套软件的模拟与使用巢软件的比较显示，尽管Spinnaker的定点算术，但是展示了用生物时间尺度和大网络尺寸的计算神经科学应用的纺纱机的可用性。对于皮质微电路模型的示例，还评估了运行时和功耗。为了获得类似于0.1 ms时间步骤的巢的准确性，与实时相比，Spinnaker需要缓慢的20倍。嵌套的运行时间使用MPI和多线程的混合并行化饱和3次。但是，实现此运行时以提高的功率和能源消耗来实现。巢的最低总能耗在144个平行线程左右到达4.6倍减速。在此设置，巢和纺纱机的每个突触事件的能量消耗相当。我们的结果拓宽了Spinnaker的应用领域，帮助指导其开发，表明必须进一步优化，例如以Concapsic为中心的网络表示，以实现大型生物网络的实时模拟。

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