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相关概念视频

Upsampling01:22

Upsampling

261
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
261

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快速SNN:通过转换量子化ANNN来快速增长的神经网络.

Yangfan Hu, Qian Zheng, Xudong Jiang

    IEEE transactions on pattern analysis and machine intelligence
    |September 18, 2023
    PubMed
    概括

    快速SNN通过在训练过程中最大限度地减少量化错误,使低延迟的高性能尖端神经网络 (SNN) 成为可能. 这种方法克服了深度SNN培训和推断速度的挑战.

    科学领域:

    • 计算机科学 计算机科学
    • 人工智能的人工智能
    • 机器学习 机器学习

    背景情况:

    • 尖端神经网络 (SNN) 提供了比人工神经网络 (ANN) 的计算和能源效率优势,这是由于事件驱动的处理和更简单的操作.
    • 训练深度SNN由于其离散的尖端性质而具有挑战性,通常需要从ANN转换为SNN.
    • ANN-to-SNN转换通常会出现量化和累积错误,导致高推理延迟和性能降低.

    研究的目的:

    • 提出Fast-SNN,这是一种用于训练深度SNN的新方法,可以在显著减少推理延迟的情况下实现高性能.
    • 通过最大限度地减少量化和序列错误来解决传统的ANN到SNN转换方法的局限性.

    主要方法:

    • 在SNN中的时间量子化和ANN中的空间量子化之间建立一个相当的映射.
    • 将量子化误差的最小化转移到量子化ANN的训练阶段.
    • 确定顺序错误是累积错误的主要原因,并引入签名的整合和火 (IF) 神经元模型和层层微调以减轻它.

    主要成果:

    • 快速SNN在各种计算机视觉任务上实现了最先进的性能,包括图像分类,对象检测和语义细分.
    • 与现有的ANN-SNN转换技术相比,提出的方法显著降低了推断延迟,同时保持了高准确度.
    • 通过优化ANN培训,证明有效地最小化量子化误差.

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    结论:

    • 快速SNN为在实际应用中部署深度SNN提供了一种可行和高效的方法.
    • 该方法成功地平衡了高性能与低延迟,实现了SNNs固有的优势.
    • 这些发现为更高效,更强大的事件驱动的神经网络架构铺平了道路.