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相关概念视频

Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

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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...
234
Convolution Properties II01:17

Convolution Properties II

174
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...
174
Convolution Properties I01:20

Convolution Properties I

140
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:
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Neural Circuits01:25

Neural Circuits

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Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
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Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

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A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of...
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Storage01:23

Storage

71
A schema is a mental framework that helps individuals organize and interpret information. Schemata, formed from previous experiences, influence how we process new information: how we encode it, the inferences we make, and how we retrieve it. For instance, a schema for what a typical classroom looks like might include desks, a teacher's desk, a whiteboard, and students in such an environment. This expectation helps us quickly understand and navigate new classrooms without needing to analyze...
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相关实验视频

Updated: Jun 9, 2025

Deep Neural Networks for Image-Based Dietary Assessment
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Deep Neural Networks for Image-Based Dietary Assessment

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一个滑动内核计算内存架构用于卷积神经网络.

Yushen Hu1, Xinying Xie1, Tengteng Lei1

  • 1State Key Laboratory of Advanced Displays and Optoelectronics Technologies, Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology (HKUST), Hong Kong, China.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|October 22, 2024
PubMed
概括
此摘要是机器生成的。

一种新的滑动内核内存计算 (SKCIM) 架构可以将内存访问量减少88%. 这种神经形态计算方法在使用卷积神经网络的手写数字分类中实现了超过95%的准确性.

关键词:
卷积计算是一种卷积计算.卷积神经网络是一种卷积神经网络.金属氧化物是一种金属氧化物.神经形态计算是一种神经形态计算.薄膜晶体管是一种薄膜晶体管.

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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction
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Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

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相关实验视频

Last Updated: Jun 9, 2025

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

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370
Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction
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Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

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395

科学领域:

  • 计算机科学 计算机科学
  • 电气工程 电气工程
  • 材料科学 材料科学 材料科学

背景情况:

  • 神经形态计算架构旨在模仿人类大脑的效率.
  • 传统系统由于存储器和处理单元之间的数据移动而面临瓶.
  • 卷积运算是深度学习的基础,但计算密集.

研究的目的:

  • 引入一种新的滑动内核内存计算 (SKCIM) 架构.
  • 为了利用低温金属氧化物薄膜晶体管 (TFT) 技术实现单体集成.
  • 为了证明SKCIM在卷积任务和神经网络应用中的效率和准确性.

主要方法:

  • 设计了一个SKCIM架构,两个重叠的功能数组用于内存和内核存储.
  • 利用低温金属氧化物薄膜晶体管 (TFT) 技术用于设备制造.
  • 实现了用于卷积任务的 32x32 SKCIM 系统和用于 MNIST 分类的 5 层卷积神经网络.

主要成果:

  • 与现有系统相比,实现了88%的内存访问操作减少.
  • 在 32x32 SKCIM 系统上成功执行了常见的卷积任务.
  • 在使用基于SKCIM的CNN的MNIST手写数字数据集上达到超过95%的准确率.

结论:

  • 在深度学习任务中,SKCIM架构为计算效率提供了显著的改进.
  • 使用低温TFT技术的单立体集成使先进的神经形态系统的实际实施成为可能.
  • SKCIM显示出加速人工智能和机器学习应用的巨大潜力.