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Updated: Jun 12, 2026

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使用渐进梯度下降进行反向传播的硬件实现,用于在现场训练多层神经网络

Eveline R W van Doremaele1,2, Tim Stevens1,2, Stijn Ringeling3

  • 1Department of Mechanical Engineering and Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven 5612AP, Netherlands.

Science advances
|July 12, 2024
PubMed
概括

这项研究引入了一种新的硬件方法,用于高效的神经网络训练,克服传统反向传播的存储限制. 该方法通过在现场逐步更新网络层来实现更快,更可扩展的人工智能.

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Gradient Vectors and Their Applications01:19

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Every point on a topographical map corresponds to a particular elevation, so the landscape can be modeled as a surface whose height depends on horizontal position. From any given location, a hiker may face infinitely many directions, but only one direction produces the fastest possible increase in elevation. This unique route is called the direction of steepest ascent, and in multivariable calculus, it is represented by the gradient vector of the elevation function.The gradient vector points...