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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?
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An important concept in studying metabolism and energy is that of chemical equilibrium. Most chemical reactions are reversible. They can proceed in both directions, releasing energy into their environment in one direction, and absorbing it from the environment in the other direction. The same is true for the chemical reactions involved in cell metabolism, such as the breaking down and building up of proteins into and from individual amino acids, respectively. Reactants within a closed system...
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In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
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随机计算 卷积神经网络架构 重新发明用于在现场可编程门数组上的高效人工智能工作负载.

Yang Yang Lee1, Zaini Abdul Halim1, Mohd Nadhir Ab Wahab2

  • 1School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Nibong Tebal, 14300 Penang, Malaysia.

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概括
此摘要是机器生成的。

本研究介绍了用于人工智能 (AI) 边缘计算的FPGA高效随机计算 (SC) 架构,为卷积神经网络 (CNN) 实现了显著的节能和更高的吞吐量. 虽然对分类有效,但SC是有效的.

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科学领域:

  • 计算机工程 计算机工程
  • 人工智能的人工智能
  • 硬件加速器 硬件加速器

背景情况:

  • 随机计算 (SC) 对ASIC上的AI边缘计算进行了充分研究,特别是对于CNN.
  • 对FPGAs的SC优化缺乏,阻碍了高效的缩放和位流聚合.
  • 现有的SC方法在FPGA实施和性能扩展方面面临挑战.

研究的目的:

  • 开发具有FPGA效率的8位SC CNN计算架构.
  • 在Kintex7 FPGA上使用新的SC设计实现完全并行的CNN模型.
  • 评估拟议的SC CNN在FPGA上的性能,准确性和能源效率.

主要方法:

  • 重新发明了FPGA高效的SC架构:SC多重复合器多次积累,多次积累函数生成器,以及二进制整形线性单元.
  • 在Kintex7 FPGA上实现完全并行的CNN模型.
  • 与二进制计算相比,对精度,节能和数据吞吐量进行评估.

主要成果:

  • 与二进制计算相比,在MNIST分类任务上实现了最小的准确性损失 (0.14%).
  • 已证明,每张图像的前至少节省了99.72%的能量.
  • 获得了比现代硬件高出31倍的数据吞吐量.
  • 早期决策终止可以实现指数级的性能增长,而准确性损失微不足道.

结论:

  • 具有FPGA效率的SC CNN对AI边缘计算非常有希望,特别是在分类任务中.
  • 拟议的SC硬件可大大节省能源,提高吞吐量.
  • SC固有的噪声限制了它对回归任务的适用性,使其不适合此类应用.