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相关概念视频

The Ideal Transformer01:26

The Ideal Transformer

909
In single-phase two-winding transformers, two windings are coiled around a magnetic core characterized by cross-sectional area A and magnetic permeability μ. A phasor current i1 enters the left winding while i2 exits the right winding, establishing the fundamental working of the transformer through electromagnetic principles.
Ampere's Law forms the basis of understanding the magnetic field within the transformer. It states that the integral of the magnetic field intensity's...
909
Types Of Transformers01:16

Types Of Transformers

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Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
If the ratio of the number of turns in the secondary winding to that of the primary winding is greater than one, then the transformer is said to be a step-up transformer. In a step-up transformer, the voltage at the secondary winding is greater than the voltage applied at the primary winding.
However, if this ratio is less than one, the transformer is said to be a step-down...
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Equivalent Circuits for Practical Transformers01:28

Equivalent Circuits for Practical Transformers

807
The practical equivalent circuits of single-phase two-winding transformers exhibit significant deviations from their idealized versions due to the inherent properties of winding resistance and finite core permeability. These properties result in real and reactive power losses, affecting the transformer's performance. Understanding these deviations is crucial for designing more efficient transformers.
In a practical transformer, each winding exhibits resistance and leakage reactance. The...
807
Neural Circuits01:25

Neural Circuits

1.6K
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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Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

213
In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the...
213
Energy Losses in Transformers01:21

Energy Losses in Transformers

981
In an ideal transformer, it is assumed that there are no energy losses, and, hence, all the power at the primary winding is transferred to the secondary winding. However, in reality,  the transformers always have some energy losses, and, hence, the output power obtained at the secondary winding is less than the input power at the primary winding due to energy losses.
There are four main reasons for energy losses in transformers.
The first cause can be  the high resistance of the...
981

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Updated: Sep 15, 2025

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矿神经形态发动机用于变压器架构.

Zhenye Zhan1, Yulu Gao2,3, Yue Liao4

  • 1Siyuan Laboratory, Guangdong Provincial Engineering Technology Research Center of Vacuum Coating Technologies and New Energy Materials, Department of Physics, Jinan University, Guangzhou, Guangdong, 510632, China.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|July 13, 2025
PubMed
概括

本研究介绍了一种矿记忆计算单元,用于高效的人工神经网络 (ANN) 硬件. 它可以实现变压器ANN的模拟处理,在显著降低能源消耗的情况下实现高性能.

关键词:
人工神经网络的人工神经网络记忆器的使用者神经形态计算是一种神经形态计算.矿器件的矿器件是什么?

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科学领域:

  • 材料科学 材料科学 材料科学
  • 计算机工程 计算机工程
  • 神经科学是一个神经科学.

背景情况:

  • 记忆计算为人工神经网络 (ANN) 提供高效的模拟乘积 (MAC) 操作.
  • 由于模拟数字数据交换,当前的记忆方法在先进的网络结构中面临效率低下的问题.
  • 变压器ANN对于先进的人工智能至关重要,它需要复杂的操作来挑战现有的记忆实现.

研究的目的:

  • 开发一种矿记忆计算单元,能够在模拟领域执行变压器ANN的所有数学运算.
  • 为了证明一个完全模拟的神经形态引擎的可行性,用于先进的AI任务.
  • 克服当前内存计算架构中数据转换的局限性.

主要方法:

  • 使用蒸汽沉积制造矿记忆计算单元,使其能够重新配置和非线性.
  • 使用配置为动态MAC,激活和软max函数的memristive细胞实现一个原型的注意力模块.
  • 构建和测试一个多层变压器网络,使用级联注意模块来执行现实任务.

主要成果:

  • 开发的存储单元成功地执行了模拟领域的变压器ANN所需的所有操作.
  • 基于该单元的神经形态引擎在RGB-T跟踪和视觉问题答案任务上实现了与GPU加速相当的性能.
  • 记忆引擎显示能耗降低98.3%,GPU的1.7%,功率效率提高了58倍.

结论:

  • 矿记忆计算单元为先进的ANN,特别是变压器模型提供了高效和准确的硬件加速的途径.
  • 在memristive设备中完全模拟处理消除了数据转换瓶,为下一代神经形态计算铺平了道路.
  • 这项工作突出了memristive设备在实现能源效率高的AI硬件复杂计算任务的潜力.