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相关概念视频

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

Ampere-Maxwell's Law: Problem-Solving

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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.
For the first part of the...
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Multimachine Stability01:25

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Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
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Parallel Processing01:20

Parallel Processing

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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...
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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...
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Micro-drive Array for Chronic in vivo Recording: Tetrode Assembly
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更新适应干扰的模拟ReRAM交叉阵列用于内存深度学习加速器.

Wooseok Choi1, Tommaso Stecconi1, Donato Francesco Falcone1

  • 1IBM Research Europe-Zurich, Rüschlikon, 8803, Switzerland.

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概括
此摘要是机器生成的。

电阻式内存 (ReRAM) 设备通过克服重量更新干扰来实现高效的内存人工智能训练. 这一突破推动了可持续,节能的人工智能加速器的发展.

关键词:
这是ReRAM的RRAM.模拟内存计算中的模拟内存计算.交叉条数组数组的交叉条数组是指一个交叉条数组.深度学习加速器深度学习加速器平行更新的重量更新

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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
  • 人工智能的人工智能

背景情况:

  • 具有交叉条数组的电阻式内存 (ReRAM) 对模拟AI加速器有很大的希望,使内存推断和训练成为可能.
  • 当前的人工智能加速通常将训练卸载给外部处理器,从而限制了功率效率.
  • 内存训练加速对于可持续的AI至关重要,但面临着诸如体重更新障碍等挑战.

研究的目的:

  • 为了解决在模拟ReRAM阵列中完全并行更新重量时的重量值干扰的挑战,用于内存训练.
  • 介绍一种新的ReRAM设备解决方案,并证明其对无干扰并行重量更新的能力.

主要方法:

  • 在HfOx层上使用导电性金属氧化物 (CMO) 的ReRAM设备,在350nm技术上使用纳米级导电丝.
  • 使用COMSOL多物理模拟分析了设备干扰耐受性,模拟了导线诱导的热电效应.
  • 在后端集成的ReRAM阵列芯片上展示了无干扰的平行重量映射.

主要成果:

  • ReRAM设备具有快速 (60 ns) 的非挥发性模拟切换.
  • 设备显示出异常的弹性更新干扰,承受超过10万脉冲.
  • 在ReRAM阵列芯片上成功展示了无干扰并行重量映射.

结论:

  • 开发的ReRAM技术为内存AI训练加速提供了可行的解决方案.
  • 对于下一代人工智能硬件来说,设备对干扰的弹性和证明的并行更新能力至关重要.
  • 硬件意识的神经网络模拟证实了深度学习加速器中完全并行的重量更新的潜力.