用信息瓶颈原理训练物理神经网络，实现高效低能耗智能计算。
Training deep physical neural networks with local physical information bottleneck
- 引入局部信息瓶颈机制，适配各类物理硬件动态
- 支持监督、无监督及强化学习，在电存器与光计算平台验证
- 可容错且支持分布式并行训练，无需数字模型辅助
深度学习已深刻改变现代社会，但面临能源与延迟的双重约束。深度物理神经网络（PNNs）是通过模拟动力学实现能效高、超快人工智能执行的互联计算系统。然而，要释放其潜力，需针对物理特性设计通用训练方法。本文提出物理信息瓶颈（PIB），一种融合信息论与局部学习的通用高效框架，使深度PNN能在任意物理动态下学习。通过为每个单元分配基于矩阵的信息瓶颈，我们在电子忆阻器芯片和光学计算平台实现了监督、无监督及强化学习。PIB还能适应严重硬件故障，并支持通过地理分布资源进行并行训练。无需辅助数字模型或对比测量，PIB将PNN训练重构为内在的、可扩展的信息理论过程，兼容多种物理载体。
原文摘要 · Abstract (English)
Deep learning has revolutionized modern society but faces growing energy and latency constraints. Deep physical neural networks (PNNs) are interconnected computing systems that directly exploit analog dynamics for energy-efficient, ultrafast AI execution. Realizing this potential, however, requires universal training methods tailored to physical intricacies. Here, we present the Physical Information Bottleneck (PIB), a general and efficient framework that integrates information theory and local learning, enabling deep PNNs to learn under arbitrary physical dynamics. By allocating matrix-based information bottlenecks to each unit, we demonstrate supervised, unsupervised, and reinforcement learning across electronic memristive chips and optical computing platforms. PIB also adapts to severe hardware faults and allows for parallel training via geographically distributed resources. Bypassing auxiliary digital models and contrastive measurements, PIB recasts PNN training as an intrinsic, scalable information-theoretic process compatible with diverse physical substrates.
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