Multi-Dimensional Reconfigurable, Physically Composable Hybrid Diffractive Optical Neural Network

• URL: http://arxiv.org/abs/2411.05748v1
• Date: Fri, 08 Nov 2024 18:08:49 GMT
• Title: Multi-Dimensional Reconfigurable, Physically Composable Hybrid Diffractive Optical Neural Network
• Authors: Ziang Yin, Yu Yao, Jeff Zhang, Jiaqi Gu,
• Abstract summary: We introduce a physically composable hybrid diffractive ONN system (MDR-HDONN) By leveraging full-system learnability, MDR-HDONN repurposes fixed fabricated optical hardware, achieving exponentially expanded functionality and superior task adaptability. MDR-HDONN has digital-comparable accuracy on various task adaptations with 74x faster speed and 194x lower energy.
• Score: 15.804251049405584
• License:
• Abstract: 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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