Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Understanding Memory01:19

Understanding Memory

358
Memory is the retention of information or experiences over time, facilitated through three main processes: encoding, storage, and retrieval. Encoding is the process of inputting information into the memory system. For instance, when listening to a lecture, watching a play, reading a book, or having a conversation, the brain is actively encoding information. This initial stage involves transforming sensory input into a form that can be processed and stored by the brain. Various factors, such as...
358
System of Memory01:23

System of Memory

6.1K
Memory is categorized into three major systems: sensory memory, short-term memory (STM), and long-term memory (LTM). These systems differ in their capacity and the duration for which they can hold information. Sensory memory captures raw sensory input from the environment, holding it for just a few seconds or less. For example, on hearing a brief, loud sound, like a car horn honking, the sound seems to linger in the mind for a moment even after it stops. This is an instance of sensory memory...
6.1K
MOS Capacitor01:25

MOS Capacitor

825
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...
825
Neural Circuits01:25

Neural Circuits

1.3K
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...
1.3K
Higher Mental Functions of Brain: Learning and Memory01:26

Higher Mental Functions of Brain: Learning and Memory

861
Memory is one of the most vital higher mental functions of the brain. Memory is closely related to learning because it enables us to retain information and experiences from our past to use them in our present life. It also helps us to remember facts, events, and skills, such as riding a bike or swimming. There are two types of memory — declarative memory, which involves memorizing facts or events, and procedural memory, which enables us to remember how to do something like writing or...
861
MOSFET: Enhancement Mode01:22

MOSFET: Enhancement Mode

374
Enhancement-mode MOSFETs are pivotal components in electronics, distinguished by their capacity to act as highly efficient switches. They are part of the larger family of metal-oxide Semiconductor Field-Effect Transistors (MOSFETs). They are available in two types: p-channel and n-channel, each tailored to specific polarity operations.
In their basic form, enhancement-mode MOSFETs are typically non-conductive when the gate-source voltage (Vgs) is zero. This default 'off' state means no...
374

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Diagnostic Perspectives on the Relationship Between Paraspinal Muscles and Bone Mineral Density: A Narrative Review.

Diagnostics (Basel, Switzerland)·2026
Same author

Microbial bile acids: Immunomodulatory signals in the tumor microenvironment.

Trends in cancer·2026
Same author

High-Rate Fingerprinting of Protein Isoforms by Quasi-regulated Enzyme-free Transport Through CytK Nanopores.

Research square·2026
Same author

Partially vertically elongated microring waveguide: simultaneously achieving a large FSR and high resonant peak intensity for an ultra-compact microring resonator.

Applied optics·2026
Same author

Cellular and molecular mechanisms of diabetes-mediated disc degeneration.

Frontiers in endocrinology·2026
Same author

SYISL promotes pulmonary epithelial-mesenchymal transition and fibrosis through DSP-Hippo/YAP pathway.

Cellular and molecular life sciences : CMLS·2026

相关实验视频

Updated: Jul 15, 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

7.8K

大量切换的基于memristor的内存计算模块用于深度神经网络训练.

Yuting Wu1, Qiwen Wang1, Ziyu Wang1

  • 1Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, 48109, USA.

Advanced materials (Deerfield Beach, Fla.)
|September 25, 2023
PubMed
概括

本研究介绍了一种用于使用基于memristor的内存计算 (CIM) 模块的深度神经网络 (DNN) 的混合精度训练方案. 这种方法加速了培训,并实现了与传统方法相比的高精度.

关键词:
深度神经网络培训深度神经网络是一个神经网络.在内存计算中的内存计算.记忆器的使用者混合精密训练是指精密训练的混合训练.

更多相关视频

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

A Method for Growing Bio-memristors from Slime Mold

Published on: November 2, 2017

9.0K
In Situ Transmission Electron Microscopy with Biasing and Fabrication of Asymmetric Crossbars Based on Mixed-Phased a-VOx
09:49

In Situ Transmission Electron Microscopy with Biasing and Fabrication of Asymmetric Crossbars Based on Mixed-Phased a-VOx

Published on: May 13, 2020

4.1K

相关实验视频

Last Updated: Jul 15, 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

7.8K
A Method for Growing Bio-memristors from Slime Mold
07:46

A Method for Growing Bio-memristors from Slime Mold

Published on: November 2, 2017

9.0K
In Situ Transmission Electron Microscopy with Biasing and Fabrication of Asymmetric Crossbars Based on Mixed-Phased a-VOx
09:49

In Situ Transmission Electron Microscopy with Biasing and Fabrication of Asymmetric Crossbars Based on Mixed-Phased a-VOx

Published on: May 13, 2020

4.1K

科学领域:

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

背景情况:

  • 深度神经网络 (DNN) 需要大量的计算资源进行训练.
  • 基于memristor的内存计算 (CIM) 为有效的DNN推断提供了潜力,但由于设备非线性和精度限制,在训练中面临挑战.

研究的目的:

  • 实验实施和验证使用基于memristor的CIM模块对DNN进行混合精度训练方案.
  • 解决基于CIM的DNN培训中的挑战,包括非线性重量更新,设备变化和低精度.

主要方法:

  • 开发了一个混合精度训练方案,使用低精度CIM模块进行加速向量矩阵乘法 (VMM) 和高精度数字单元来积累重量更新.
  • 只有当累积的重量变化超过一个值时,Memristor设备才会更新.
  • 该方案在集成模拟CIM模块和数字子系统的芯片系统上实施.

主要成果:

  • 拟议的方案显示了LeNet培训的快速融合,达到97.73%的准确性.
  • 使用现实的硬件参数进行的评估证实了CIM模块对于高效的混合精度DNN训练的有效性.
  • 在芯片上训练的模型显示出对硬件变化的稳定性,使其能够直接部署在CIM推理芯片上.

结论:

  • 使用基于memristor的CIM模块进行混合精度训练,可实现高效准确的DNN训练.
  • 这种方法减轻了硬件变化,并简化了在CIM推理硬件上部署训练模型.
  • 开发的芯片上的系统促进了先进的人工智能模型的实际实施.