光子计算与机器学习交叉领域的研究者值得关注——SPIM实现EP算法为低功耗训练开辟了新方向，尤其适合对能效敏感的硬件部署场景。
研究人员利用空间光子伊辛机（SPIM）实现了平衡传播（EP）算法的混合光学-数字实现。该方案通过空间光调制器以相位调制方式光学编码连续神经元状态和秩1二进制可训练模式，并用有限差分法完成推理。实验在Wine分类数据集上验证了系统可行性，数值模拟进一步展示了连续耦合和结构化耦合矩阵在MNIST数据集上的潜力。这项工作为低能耗的物理实现平衡传播提供了具体路径。
Optical Implementation of Equilibrium Propagation Using Spatial Photonic Ising Machines
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.