Abstract
Realization of deep learning with coherent diffraction has achieved remarkable development nowadays, which benefits on the fact that matrix multiplication can be optically executed in parallel with high band-with and low latency. Coherent optical field in the form of complex-valued entity can be manipulated into a task-oriented output. In this paper, a modulation mechanism is established by implementing the equivalence between a digital deep unitary neural network and optical coherent diffraction. We present a unitary learning avenue on diffractive deep neural network, meeting the physical unitary prior in coherent diffraction. The Unitary learning is a Backpropagation serving to unitary weights update through the gradient translation from Euclidean to Riemannian space. The temporal-space evolution characteristics in unitary learning are formulated and elucidated. And a compatible condition on how to select the nonlinear activation in complex space is unveiled, encapsulating the fundamental sigmoid, tanh and quasi-ReLu in complex space available in a single channel training. The performance of phase-ReLu is particularly emphasized. As a preliminary application, diffractive deep neural network with unitary learning is tentatively implemented on the 2D classification and verification tasks.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.