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相关概念视频

Neural Circuits01:25

Neural Circuits

2.5K
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...
2.5K
Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

756
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...
756
Convolution Properties II01:17

Convolution Properties II

522
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...
522
Convolution Properties I01:20

Convolution Properties I

494
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:
494
MOS Capacitor01:25

MOS Capacitor

1.4K
A Metal-Oxide-Semiconductor (MOS) capacitor is a fundamental structure used extensively in semiconductor device technology, particularly in the fabrication of integrated circuits and MOSFETs (metal-oxide-semiconductor field-effect transistors). The MOS capacitor consists of three layers: a metal gate, a dielectric oxide, and a semiconductor substrate.
The metal gate is typically made from highly conductive materials such as aluminum or polysilicon. Beneath the metal gate lies a thin layer of...
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相关实验视频

Updated: Dec 29, 2025

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes
08:07

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes

Published on: March 9, 2019

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完全硬件实现的卷积神经网络

Peng Yao1, Huaqiang Wu2,3, Bin Gao1,4

  • 1Institute of Microelectronics, Beijing Innovation Center for Future Chips (ICFC), Tsinghua University, Beijing, China.

Nature
|January 31, 2020
PubMed
概括
此摘要是机器生成的。

高性能的memristor交叉阵列可以高效地实现卷积神经网络 (CNN). 与GPU相比,这种神经形态系统在图像识别任务中实现了超过96%的准确性.

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A Method for Growing Bio-memristors from Slime Mold
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相关实验视频

Last Updated: Dec 29, 2025

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes
08:07

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes

Published on: March 9, 2019

8.2K
A Method for Growing Bio-memristors from Slime Mold
07:46

A Method for Growing Bio-memristors from Slime Mold

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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

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科学领域:

  • 神经形态工程
  • 材料科学
  • 计算机科学

背景情况:

  • 基于memristor的神经形态计算提供了快速,节能的神经网络训练.
  • 由于设备的缺陷,使用memristor横杆的卷积神经网络 (CNN) 的硬件实现仍然具有挑战性.
  • 在基于memristor的CNN中,由于设备的可变性和低产量,很难获得与软件可比的结果.

研究的目的:

  • 制造高产量,统一的记忆器交叉阵列用于CNN的实现.
  • 开发一种混合训练方法来克服设备的非理想性.
  • 展示一个可扩展的基于memristor的CNN用于图像识别和边缘计算.

主要方法:

  • 集成的记忆器交叉阵列的制造 (八个2048个细胞阵列).
  • 开发和应用混合培训方法以适应设备的变化.
  • 实现基于五层memristor的CNN用于MNIST图像识别.

主要成果:

  • 使用基于memristor的CNN实现了MNIST图像识别的高精度 (> 96%).
  • 证明了并行处理能力,包括并行卷曲和同时处理不同的输入.
  • 比最先进的GPU高出两倍的能效.
  • 展示了像残余神经网络这样的大型神经网络架构的可扩展性.

结论:

  • 可行的基于memristor的非·诺伊曼硬件解决方案可用于深度神经网络.
  • 开发的系统为节能边缘计算应用提供了有前途的途径.
  • 高性能和统一的memristor阵列,加上适应性训练,克服了硬件CNN实现的先前限制.