While backpropagation and automatic differentiation have driven deep learning's success, the physical limits of chip manufacturing and rising environmental costs of deep learning motivate alternative learning paradigms such as physical neural networks. However, most existing physical neural networks still rely on digital computing for training, largely because backpropagation and automatic differentiation are difficult to realize in physical systems. We introduce FFzero, a forward-only learning framework enabling stable neural network training without backpropagation or automatic differentiation. FFzero combines layer-wise local learning, prototype-based representations, and directional-derivative-based optimization through forward evaluations only. We show that local learning is effective under forward-only optimization, where backpropagation fails. FFzero generalizes to multilayer perceptron and convolutional neural networks across classification and regression. Using a simulated photonic neural network as an example, we demonstrate that FFzero provides a viable path toward backpropagation-free in-situ physical learning.

翻译：尽管反向传播和自动微分推动了深度学习的成功，但芯片制造的物理极限及深度学习日益增长的环境成本，催生了物理神经网络等替代学习范式。然而，现有大多数物理神经网络仍依赖数字计算进行训练，这主要是因为反向传播和自动微分在物理系统中难以实现。我们提出FFzero——一种仅前向学习框架，能够在无需反向传播或自动微分的情况下实现稳定的神经网络训练。该框架通过仅前向评估，结合逐层局部学习、基于原型的表征及方向导数优化。研究表明，在反向传播失效的情况下，局部学习在仅前向优化中依然有效。FFzero可推广至用于分类与回归任务的多层感知机及卷积神经网络。以模拟光子神经网络为例，我们证明FFzero为无反向传播的原位物理学习提供了可行路径。