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深层物理神经网络的无反向传播训练
Ali Momeni1, Babak Rahmani2, Matthieu Malléjac1
1Laboratory of Wave Engineering, Department of Electrical Engineering, EPFL, Lausanne CH-1015, Switzerland.
Science (New York, N.Y.)
|November 23, 2023
概括
我们开发了一个物理局部学习 (PhyLL) 算法来训练物理神经网络. 这种方法提高了训练速度和稳定性,同时降低了深度学习硬件的功耗.
科学领域:
- 人工智能
- 深度学习
- 物理计算
背景情况:
- 深度学习模型在增加尺寸时取得成功,但面临能源和可扩展性挑战.
- 目前的数字深度学习培训方法,如反向传播,不适合实体硬件实现.
研究的目的:
- 提出一个新的物理局部学习 (PhyLL) 算法来训练深层物理神经网络.
- 允许对物理神经网络进行监督和无监督的训练,而不需要详细了解它们的非线性特性.
主要方法:
- 引入了与PhyLL算法集成的简单深度神经网络架构.
- 训练了基于波的多种物理神经网络,使用PhyLL进行元音和图像分类任务.
主要成果:
- 在不同的物理神经网络配置中展示了PhyLL方法的普遍性.
- 与现有的硬件意识培训方案相比,提高了培训速度,提高了稳定性和降低了功耗.
结论:
- PhyLL算法为训练物理神经网络提供了一种高效且对硬件友好的方法.
- 这种方法克服了物理实现的传统反向传播的局限性,减少了数字计算和能源使用.
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