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Fast and Efficient Information Transmission with Burst Spikes in Deep Spiking Neural Networks

机译:深度尖峰神经网络中突发尖峰的快速高效信息传输

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摘要

Spiking neural networks (SNNs) are considered as one of the most promising artificial neural networks due to their energy-efficient computing capability. Recently, conversion of a trained deep neural network to an SNN has improved the accuracy of deep SNNs. However, most of the previous studies have not achieved satisfactory results in terms of inference speed and energy efficiency. In this paper, we propose a fast and energy-efficient information transmission method with burst spikes and hybrid neural coding scheme in deep SNNs. Our experimental results showed the proposed methods can improve inference energy efficiency and shorten the latency.
机译:尖峰神经网络(SNN)由于具有高能效的计算能力,被认为是最有前途的人工神经网络之一。最近,将经过训练的深度神经网络转换为SNN已提高了深度SNN的准确性。但是,大多数先前的研究在推理速度和能效方面均未取得令人满意的结果。在本文中,我们提出了一种在深层SNN中具有突发尖峰和混合神经编码方案的快速,节能的信息传输方法。我们的实验结果表明,所提出的方法可以提高推理能量效率并缩短等待时间。

著录项

  • 来源
  • 会议地点 Las Vegas(US)
  • 作者单位

    Department of Electrical and Computer Engineering, ASRI, INMC, and Institute of Engineering Research Seoul National University, Seoul 08826, South Korea;

    Department of Electrical and Computer Engineering, ASRI, INMC, and Institute of Engineering Research Seoul National University, Seoul 08826, South Korea;

    Department of Electrical and Computer Engineering, ASRI, INMC, and Institute of Engineering Research Seoul National University, Seoul 08826, South Korea;

    Department of Electrical and Computer Engineering, ASRI, INMC, and Institute of Engineering Research Seoul National University, Seoul 08826, South Korea;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Encoding; Neurons; Energy efficiency; Information processing; Biological neural networks; Training; Oscillators;

    机译:编码;神经元;能效;信息处理;生物神经网络;训练;振荡器;;

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