可物理组合的可重构光学神经网络，实现超快低耗且灵活适应多种任务。
Multi-Dimensional Reconfigurable, Physically Composable Hybrid Diffractive Optical Neural Network
- 通过可学习系统变量重构固定光路，实现光学硬件的动态复用。
- 比传统光学神经网络快74倍、能效高194倍，精度接近数字计算。
- 适合需要高速低功耗推理的AI应用，如边缘设备和实时处理。
衍射光学神经网络（DONNs）利用自由空间光波传播实现超并行、高效率计算，是极具前景的人工智能加速器。然而，其固定光学结构在制造后无法重构，难以应对动态AI工作负载和不断演进的应用需求。为此，我们首次提出多维可重构混合衍射光学神经网络系统（MDR-HDONN），一种可物理组合的架构，为DONNs引入全新自由度与前所未有的灵活性。通过全系统可学习性，MDR-HDONN重新利用已制造的固定光学硬件，借助系统变量的可微学习，实现功能指数级扩展与卓越的任务适应能力。此外，该系统采用混合光/光子设计，融合集成光子学的可重构性与自由空间衍射系统的超并行性。大量实验证明，MDR-HDONN在多种任务适配中达到接近数字计算的精度，速度提升74倍，能耗降低194倍。相比以往DONNs，其功能空间呈指数级扩大，训练速度更快5倍，为多功能、可组合、混合光/光子人工智能计算开辟新范式。代码将开源。
原文摘要 · Abstract (English)
Diffractive optical neural networks (DONNs), leveraging free-space light wave propagation for ultra-parallel, high-efficiency computing, have emerged as promising artificial intelligence (AI) accelerators. However, their inherent lack of reconfigurability due to fixed optical structures post-fabrication hinders practical deployment in the face of dynamic AI workloads and evolving applications. To overcome this challenge, we introduce, for the first time, a multi-dimensional reconfigurable hybrid diffractive ONN system (MDR-HDONN), a physically composable architecture that unlocks a new degree of freedom and unprecedented versatility in DONNs. By leveraging full-system learnability, MDR-HDONN repurposes fixed fabricated optical hardware, achieving exponentially expanded functionality and superior task adaptability through the differentiable learning of system variables. Furthermore, MDR-HDONN adopts a hybrid optical/photonic design, combining the reconfigurability of integrated photonics with the ultra-parallelism of free-space diffractive systems. Extensive evaluations demonstrate that MDR-HDONN has digital-comparable accuracy on various task adaptations with 74x faster speed and 194x lower energy. Compared to prior DONNs, MDR-HDONN shows exponentially larger functional space with 5x faster training speed, paving the way for a new paradigm of versatile, composable, hybrid optical/photonic AI computing. We will open-source our codes.
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