• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Scalable Digital Neuromorphic Architecture for Large-Scale Biophysically Meaningful Neural Network With Multi-Compartment Neurons
【24h】

Scalable Digital Neuromorphic Architecture for Large-Scale Biophysically Meaningful Neural Network With Multi-Compartment Neurons

机译：具有多格神经元的大规模生物物理意义的神经网络的可扩展数字神经形态架构。

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Multicompartment emulation is an essential step to enhance the biological realism of neuromorphic systems and to further understand the computational power of neurons. In this paper, we present a hardware efficient, scalable, and real-time computing strategy for the implementation of large-scale biologically meaningful neural networks with one million multi-compartment neurons (CMNs). The hardware platform uses four Altera Stratix III field-programmable gate arrays, and both the cellular and the network levels are considered, which provides an efficient implementation of a large-scale spiking neural network with biophysically plausible dynamics. At the cellular level, a cost-efficient multi-CMN model is presented, which can reproduce the detailed neuronal dynamics with representative neuronal morphology. A set of efficient neuromorphic techniques for single-CMN implementation are presented with all the hardware cost of memory and multiplier resources removed and with hardware performance of computational speed enhanced by 56.59% in comparison with the classical digital implementation method. At the network level, a scalable network-on-chip (NoC) architecture is proposed with a novel routing algorithm to enhance the NoC performance including throughput and computational latency, leading to higher computational efficiency and capability in comparison with state-of-the-art projects. The experimental results demonstrate that the proposed work can provide an efficient model and architecture for large-scale biologically meaningful networks, while the hardware synthesis results demonstrate low area utilization and high computational speed that supports the scalability of the approach.
机译：多隔室仿真是增强神经形态系统的生物现实性并进一步了解神经元的计算能力的重要步骤。在本文中，我们提出了一种硬件有效，可扩展的实时计算策略，用于实现具有一百万个多隔室神经元（CMN）的大规模生物学意义的神经网络。硬件平台使用四个Altera Stratix III现场可编程门阵列，并且考虑了蜂窝和网络级别，这提供了具有生物物理上合理的动力学的大规模尖峰神经网络的有效实现。在细胞水平上，提出了一种具有成本效益的多CMN模型，该模型可以再现具有代表性神经元形态的详细神经元动力学。与传统的数字实现方法相比，本文提出了一套有效的用于单CMN实现的神经形态技术，消除了所有的硬件内存成本和乘法器资源，并且计算速度的硬件性能提高了56.59％。在网络级别，提出了一种具有新型路由算法的可扩展片上网络（NoC）体系结构，以增强NoC性能，包括吞吐量和计算延迟，与最新状态相比，具有更高的计算效率和功能。艺术项目。实验结果表明，所提出的工作可以为大规模的具有生物学意义的网络提供有效的模型和体系结构，而硬件综合结果表明，该方法具有较低的面积利用率和较高的计算速度，可支持该方法的可扩展性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

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