在物质中的梯度下降通过同质梯度提取
Marcus N Boon1,2,3, Lorenzo Cassola1,4, Hans-Christian Ruiz Euler1
1NanoElectronics Group, MESA+ Institute for Nanotechnology and BRAINS Center for Brain-Inspired Computing, University of Twente, Enschede, The Netherlands.
Nature communications
|November 21, 2025
概括
研究人员开发了一种新型的同位素检测方法,用于物理系统中高效的梯度下降. 这种方法绕过了复杂的计算,在专门的硬件和材料系统中实现了更快,更节能的学习.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 材料科学 材料科学 材料科学
背景情况:
- 深度学习,一种由大脑启发的神经网络方法,已经改变了机器学习.
- 逆向传播对于深度学习至关重要,在数字系统中是计算密集型和耗能型.
- 目前用于AI学习的专用硬件通常涉及复杂和缓慢的训练方法.
研究的目的:
- 在物理计算系统中开发一种简化和节能的梯度提取方法.
- 为了在硬件中实现直接的梯度下降,而不需要分析模型.
- 促进创建自主学习材料系统.
主要方法:
- 一种新的梯度提取技术,利用同位素检测.
- 扰乱系统参数的不同频率的正弦波形.
- 将该方法应用于可重新配置的非线性处理单元.
主要成果:
- 实现了强大而可扩展的梯度信息提取.
- 在物理系统中直接展示了梯度下降.
- 该方法在非线性处理单元中被证明是有效的.
结论:
- 同体梯度提取为传统反向传播提供了更简单,更快,更节能的替代方案.
- 该技术广泛适用于各种物理系统和专用硬件.
- 在材料中充分实施的潜力,使自学材料系统成为可能.
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