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相关概念视频

Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

229
In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
229
Convolution Properties II01:17

Convolution Properties II

173
The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
173
Convolution Properties I01:20

Convolution Properties I

137
Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
137

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相关实验视频

Updated: Jun 5, 2025

Characterization of SiN Integrated Optical Phased Arrays on a Wafer-Scale Test Station
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Published on: April 1, 2020

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基于阵列波导网格的光学神经网络的无冗余集成光学卷轴器.

Shiji Zhang1, Haojun Zhou1, Bo Wu1

  • 1Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China.

Nanophotonics (Berlin, Germany)
|December 5, 2024
PubMed
概括

这项研究介绍了一种用于人工智能的新型光学卷积架构. 它显著减少了冗余,并提高了光学神经网络 (ONN) 的效率,以实现更快的AI计算.

关键词:
卷积的卷积 卷积的卷积通过光学计算计算.一个光学神经网络.光子学是一种光子学.

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Optical Recording of Suprathreshold Neural Activity with Single-cell and Single-spike Resolution
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Multimodal Volumetric Retinal Imaging by Oblique Scanning Laser Ophthalmoscopy oSLO and Optical Coherence Tomography OCT
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Multimodal Volumetric Retinal Imaging by Oblique Scanning Laser Ophthalmoscopy oSLO and Optical Coherence Tomography OCT

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相关实验视频

Last Updated: Jun 5, 2025

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Published on: April 1, 2020

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Optical Recording of Suprathreshold Neural Activity with Single-cell and Single-spike Resolution
08:48

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科学领域:

  • 光电学是指光电子产品.
  • 人工智能的人工智能
  • 计算机工程 计算机工程

背景情况:

  • 光学神经网络 (ONN) 为人工智能提供高速和节能计算.
  • 光学卷积至关重要,但目前的架构遭受了输入冗余和资源浪费.

研究的目的:

  • 开发一个集成的光学卷积架构,消除冗余.
  • 为了利用阵列波导网格 (AWG) 进行高效的卷积操作.

主要方法:

  • 使用AWG原则设计了一个集成的光学卷积架构.
  • 实现了一个执行M x N乘积-积累运算的系统,每周期使用M + N单位.
  • 通过手写数字识别实验验验证了架构.

主要成果:

  • 在手写数字识别中实现了5位精度和91.9%的准确性.
  • 展示了一个没有冗余的架构,低功耗.
  • 报告的高计算密度为8.53 teraOP 毫米-1 秒-1.1.

结论:

  • 开发的基于AWG的光学卷积架构显著减少了冗余并提高了效率.
  • 该方法为光学神经网络提供了可扩展,低功耗和高密度的解决方案.
  • 这项工作通过优化的光学硬件来推进高性能计算和AI应用.