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相关概念视频

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

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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.
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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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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.
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An electric field suffers a discontinuity at a surface charge. Similarly, a magnetic field is discontinuous at a surface current. The perpendicular component of a magnetic field is continuous across the interface of two magnetic mediums. In contrast, its parallel component, perpendicular to the current, is discontinuous by the amount equal to the product of the vacuum permeability and the surface current. Like the scalar potential in electrostatics, the vector potential is also continuous...
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CMOS加上随机纳米磁铁使异质计算机能够进行概率推理和学习.

Nihal Sanjay Singh1, Keito Kobayashi1,2,3, Qixuan Cao1

  • 1Department of Electrical and Computer Engineering, University of California Santa Barbara, Santa Barbara, 93106, CA, USA.

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概括

研究人员将随机磁道连接 (sMTJ) 概率位 (p-bit) 与FPGA集成,创建了一个节能原型. 这一进步通过减少能源消耗和晶体管数量来增强概率计算和机器学习.

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科学领域:

  • 计算机工程 计算机工程
  • 材料科学 材料科学 材料科学
  • 人工智能的人工智能

背景情况:

  • 摩尔定律正在通过将新兴纳米技术与CMOS晶体管集成来扩展.
  • 概率机器学习,优化和量子模拟通常依赖于蒙特卡洛算法.
  • 静态磁道连接 (sMTJs) 为新的计算范式提供了一个有希望的途径.

研究的目的:

  • 开发一种节能原型,将CMOS技术与基于sMTJ的概率位 (p-bits) 结合起来.
  • 为了证明这种混合系统对概率推理和学习的能力.
  • 与传统的CMOS晶体管相比,评估sMTJ p-bit的性能和能源效率.

主要方法:

  • 集成基于sMTJ的p-bits与现场可编程门数组 (FPGA).
  • 使用由sMTJs控制的异步驱动的CMOS电路.
  • 利用吉布斯采样的算法更新-顺序-不变性属性进行概率推理.
  • 增加低质量的随机数生成器 (RNG) 与sMTJ随机性.

主要成果:

  • 成功创建了一个功能性的CMOS+sMTJ原型.
  • 该系统展示了有效的概率推断和学习能力.
  • sMTJ p-bit被证明可以取代多达10,000个CMOS晶体管,其能量消耗量减少两倍.
  • sMTJs的随机性可以提高随机数生成的质量.

结论:

  • 开发的CMOS + sMTJ原型在节能概率计算方面取得了重大进展.
  • 这种方法可以提高深波兹曼机器和其他基于能量的学习算法的性能.
  • 将sMTJ与FPGA集成为高吞吐量和节能概率机器学习和模拟铺平了道路.