CHORD: Composable Hybrid Optical Reconfigurable Diffractive Framework For Optical Neural Network
Ziang Yin, Yao Yu, Jeff Jun Zhang, Jiaqi Gu · 2025
Diffractive optical neural networks (DONNs), leveraging freespace 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 postfabrication hinders practical deployment in the face of dynamic AI workloads and evolving applications. To overcome this challenge, we introduce, for the first time, a composable hybrid optical reconfigurable diffractive framework (CHORD), a physically composable architecture that unlocks a new degree of freedom and unprecedented versatility in DONNs. By leveraging full-system learnability, CHORD repurposes fixed fabricated optical hardware, achieving exponentially expanded functionality and superior task adaptability through the differentiable learning of system variables. Furthermore, CHORD 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 CHORD has digital-comparable accuracy on various task adaptations with $74 \times$ faster speed and $194 \times$ lower energy. Compared to prior DONNs, CHORD shows exponentially larger functional space with $5 \times$ faster training speed, paving the way for a new paradigm of versatile, composable, hybrid optical/photonic AI computing. Our code is open-sourced at link1.1github.com/ScopeX-ASU/CHORD