用光子伊辛机实现平衡传播，提升能效。
Optical Implementation of Equilibrium Propagation Using Spatial Photonic Ising Machines
- 光子伊辛机通过相位调制编码神经元状态与可训练模式。
- 在葡萄酒分类数据集上验证，复杂度更高的手写数字识别也表现良好。
- 适合追求低功耗物理计算的神经网络研究者。
平衡传播为基于能量的网络训练提供了有前景的替代方案。本文展示了一种基于空间光子伊辛机（SPIM）的混合光学-数字实现方法。SPIM利用规范变换方法，通过空间光调制器将连续神经元状态和秩1二值可训练模式作为相位调制进行光学编码，推理过程采用有限差分法实现。实验系统在葡萄酒分类数据集上进行了评估。数值模拟进一步验证了该方法在更复杂的MNIST数据集上的潜力，包括连续耦合和结构化耦合矩阵的应用。本工作为平衡传播的节能物理实现提供了具体路径。
原文摘要 · Abstract (English)
Equilibrium Propagation offers a compelling alternative to traditional machine learning for training energy-based networks. Here we demonstrate a hybrid optical-digital implementation of EP using a Spatial Photonic Ising Machine (SPIM). The SPIM exploits the gauge transformation method to optically encode both continuous neuron states and rank-1 binary trainable patterns as phase modulations via a spatial light modulator, with inference realized using a finite difference scheme. The experimental system is evaluated on the Wine classification dataset. The potential of this approach, including the use of continuous couplings and structured coupling matrices, is evaluated numerically on the more complex MNIST dataset. Our work provides a concrete pathway toward energy-efficient physical implementations of Equilibrium Propagation.
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