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

Parallel Processing01:20

Parallel Processing

252
The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
252
MOSFET: Enhancement Mode01:22

MOSFET: Enhancement Mode

493
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...
493
Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

774
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...
774

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

The growth factor FGF21 maintains neuromuscular junction through histone deacetylase HDAC4 in denervation-induced skeletal muscle atrophy.

The Journal of biological chemistry·2025
Same author

Specific construction of asymmetric carbon-nickel-chlorine single-atom sites via carbon vacancy engineering for efficient CO<sub>2</sub> electroreduction.

Nature communications·2025
Same author

Unveiling the Influence of Formation Voltage on Li-Rich Layered Oxide Cathode.

Angewandte Chemie (International ed. in English)·2025
Same author

A carbon-based single-atom Cu electrocatalyst for efficient conversion of CO<sub>2</sub> to carbon products.

Chemical communications (Cambridge, England)·2025
Same author

Pt Atomic Site-Engineered Redox Mediator Boosts Electrosynthesis of Formic Acid from Glycerol.

Angewandte Chemie (International ed. in English)·2025
Same author

PicoSleepNet: An Ultra Lightweight Sleep Stage Classification by Spike Neural Network Using Single-Channel EEG Signal.

IEEE journal of biomedical and health informatics·2025
Same journal

Multiplexed Crossbar GFET Array With BioADC for Multi-Modal Aptamer-Based Sensing.

IEEE transactions on biomedical circuits and systems·2026
Same journal

A VPG-Based Adaptive Windowing PPG Sensor IC for Low-Power Wearable Monitoring.

IEEE transactions on biomedical circuits and systems·2026
Same journal

A Chopper Amplifier with Feedforward SAR ADC Assisted DC Servo Loop Achieving ±1V DC Offset Cancellation in 2.1s for Neural Signal Recordings.

IEEE transactions on biomedical circuits and systems·2026
Same journal

ANP-R: A 22nm 0.88pJ/SOP Asynchronous SNN-based Processor with Coarse-Grained Reconfigurable Architecture Enabling Multisensory On-chip Incremental Learning for Edge AI.

IEEE transactions on biomedical circuits and systems·2026
Same journal

A High-Efficiency Neural Processing SoC for Adaptive Closed-Loop Neuromodulation.

IEEE transactions on biomedical circuits and systems·2026
Same journal

DustNet: A Wireless Network of Ultrasonic Neural Implants.

IEEE transactions on biomedical circuits and systems·2026
查看所有相关文章

相关实验视频

Updated: Sep 18, 2025

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

5.1K

一个0.66-mm20.49 pJ/SOP SNN处理器与时空后神经处理和模型适应交叉杆在40纳米CMOS.

Jinqiao Yang, Zikai Zhu, Haoming Chu

    IEEE transactions on biomedical circuits and systems
    |June 24, 2025
    PubMed
    概括
    此摘要是机器生成的。

    本研究介绍了用于AIoT设备的新型尖端神经网络 (SNN) 处理器,提高了能源效率和模型适应性. 它的创新设计在一个紧的形状因素中实现了卓越的性能.

    更多相关视频

    Probing C84-embedded Si Substrate Using Scanning Probe Microscopy and Molecular Dynamics
    13:58

    Probing C84-embedded Si Substrate Using Scanning Probe Microscopy and Molecular Dynamics

    Published on: September 28, 2016

    11.9K
    Characterization of SiN Integrated Optical Phased Arrays on a Wafer-Scale Test Station
    05:57

    Characterization of SiN Integrated Optical Phased Arrays on a Wafer-Scale Test Station

    Published on: April 1, 2020

    8.1K

    相关实验视频

    Last Updated: Sep 18, 2025

    Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
    09:44

    Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

    Published on: March 8, 2024

    5.1K
    Probing C84-embedded Si Substrate Using Scanning Probe Microscopy and Molecular Dynamics
    13:58

    Probing C84-embedded Si Substrate Using Scanning Probe Microscopy and Molecular Dynamics

    Published on: September 28, 2016

    11.9K
    Characterization of SiN Integrated Optical Phased Arrays on a Wafer-Scale Test Station
    05:57

    Characterization of SiN Integrated Optical Phased Arrays on a Wafer-Scale Test Station

    Published on: April 1, 2020

    8.1K

    科学领域:

    • 计算机工程 计算机工程
    • 人工智能的人工智能
    • 神经形态计算是一种神经形态计算.

    背景情况:

    • 尖端神经网络 (SNN) 的现有并行架构在能源效率,模型适应性和AIoT应用的面积足迹方面存在局限性.
    • 为SNN开发紧而高效的硬件对于推进边缘AI能力至关重要.

    研究的目的:

    • 介绍一款新型SNN处理器,旨在实现高能效和模型适应性,在一个紧的区域内为AIoT提供足迹.
    • 克服当前并行架构在电力消耗和灵活性方面的局限性.

    主要方法:

    • 实施一个时空后神经处理 (PoNP) 方案,以有效地重用膜潜力和并行性.
    • 整合了模型适应交叉杆设计与动态切换,用于多功能SNN模型处理.
    • 使用8路平行管道架构来提高吞吐量.

    主要成果:

    • 每个周期实现了128个Synaptic操作 (SOPs) 的吞吐量,能源效率提高了2.8倍.
    • 该芯片采用40纳米CMOS工艺制造,占地0.66毫米2,消耗6.26毫瓦.
    • 在各种SNN数据集中展示了最好的能源效率 (0.49 pJ/SOP) 和低延迟.

    结论:

    • 开发的SNN处理器在能源效率和AIoT的适应性方面取得了重大进展.
    • PoNP方案和模型适应交叉杆设计是实现卓越性能的关键创新.
    • 这项工作在功耗和处理能力方面为神经形态硬件设定了新的基准.