Scientific Computing with Diffractive Optical Neural Networks

Machine learning (ML) has demonstrated the state-of-the-art performance in a variety of applications, such as computer vision (1, 2), medicine (3), finance (4), autonomous engineering design (5), and scientific computing (6, 7), but performing ML tasks on hardware systems requires substantial energy and computational resources. The fundamental quantum mechanics limit leads to a bottleneck of reducing the energy consumption and simultaneously increasing the integration density of electronic circuits to catch up with the increasing scale of modern large-scale ML models (8,9). Optical architectures are emerging as promising high-throughput and energy-efficient ML hardware accelerators by leveraging the parallelism and low static energy consumption of a fundamentally different particle, photon, for computing (10, 11). Among optical systems, free-space diffractive optical neural networks (DONNs) that are able to host millions of computing neurons and form deep neural network architectures can optically perform ML tasks through the spatial light modulation and optical diffraction of coherent light in multiple diffractive layers (12-30). Prior demonstrations have shown the capability of DONNs systems to recognize input images directly in optical domain.