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Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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The z and the Student t distribution estimate the population mean using the sample mean and standard deviation. However, to decide which distribution to use for a calculation, one needs to determine the sample size, the nature of the distribution, and whether the population standard deviation is known. If the population standard deviation is known and the population is normally distributed, or if the sample size is greater than 30, the z distribution is preferred. The Student t distribution is...
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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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基于Hessian的混合精度量化与神经网络的过渡意识培训.

Zhiyong Huang1, Xiao Han1, Zhi Yu1

  • 1School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China.

Neural networks : the official journal of the International Neural Network Society
|November 23, 2024
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概括

基于Hessian的混合精度量化意识训练 (HMQAT) 减少了寻找最佳神经网络位配置的搜索时间. 这种方法可以实现显著的模型大小减少,同时保持高精度,使嵌入式设备的有效部署成为可能.

关键词:
黑塞尼亚人 黑塞尼亚人混合精度的精度混合精度.模型的压缩压缩.定量化意识培训培训是如何实现的量子化神经网络 量子化神经网络

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科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 模型量化对于在资源受限的嵌入式系统上部署深度神经网络至关重要.
  • 混合精度量化提供了性能优势,但通常需要对最佳位配置进行广泛的搜索,从而增加计算成本.

研究的目的:

  • 引入基于Hessian的混合精度定量化意识培训 (HMQAT),以减少在定量化神经网络中寻找最佳位配置的搜索开销.
  • 开发一种有效的方法来确定最佳的混合精度设置,以平衡精度和模型大小.

主要方法:

  • HMQAT使用基于联合平均赫西安痕迹和参数大小的灵敏度指标来指导位配置搜索.
  • 使用自动化的帕雷托边界方法来解决位配置优化问题.
  • 量化过渡意识微调的尺度因子被纳入以保持推断性能.

主要成果:

  • HMQAT显著减少了混合精度定量化的搜索开销.
  • 在ImageNet和CIFAR10上的评估显示了显著的模型大小减少 (例如,ImageNet上的ResNet18的10.34x),同时保持了高的Top-1精度 (99.81%).
  • 该方法在搜索成本和精度大小的权衡方面优于现有的最先进的混合精度技术.

结论:

  • HMQAT为混合精度量子化意识培训提供了一种高效和有效的方法.
  • 拟议的方法可以实现神经网络的高级压缩,从而促进它们在轻量级设备上部署.
  • 这项研究有助于推进嵌入式应用程序的高效深度学习推理.