All-optical computation of a group of transformations using polarization-encoded diffractive network

LOS ANGELES - May 25, 2022 - PRLog -- Implementing large-scale linear transformations or matrix computations plays a pivotal role in modern information processing systems. Digital computer systems need to complete up to billions of matrix operations per second to perform complex computational tasks, such as training and inference for deep neural networks. As a result, the throughput of linear transform computations can directly influence the performance and capacity of the underlying computing systems. These linear transformations are computed using digital processors in computers, which can face bottlenecks as the size of the data to be processed gets larger and larger. This is where all-optical computing methods can potentially provide a remedy through their parallelism and speed.

In a recent study published in Light: Science and Applications (https://doi.org/10.1038/s41377-022-00849-x), researchers from the University of California, Los Angeles (UCLA) have demonstrated a polarization-encoded diffractive optical processor to enable high-speed, low-power computation of multiple linear transformations using only the diffraction of light. This optical processor utilizes a series of structured diffractive surfaces and simple polarizer arrays, which can jointly manipulate the input light and generate, at the output plane, the result of any desired complex-valued linear transformation of the input field. A major advantage of this approach is that, except for the illumination light, it does not need any computing power and can be scaled up to handle large input data. In addition, all the computation is completed at the speed of light, making the execution of complex-valued linear transformations extremely fast.

This research was led by Professor Aydogan Ozcan from the Electrical and Computer Engineering Department and California NanoSystems Institute (CNSI) at UCLA. This new optical architecture introduces a polarization encoding mechanism that allows a single diffractive processor to perform up to four different linear transformations through polarization multiplexing of information. By enabling the structured surfaces to communicate with the polarization elements embedded in the diffractive volume, a single diffractive optical processor can implicitly form multiple distinct computation channels, each of which can be accessed using a specific combination of the input and output polarization states. After being trained through data-driven approaches such as deep learning, the diffractive processor can all-optically compute a group of complex-valued linear transformations, which can be assigned to perform different computational tasks, including, for example, image classification, segmentation, encryption, and filtering operations. This unique design allows a single diffractive optical processor to be loaded with a diverse range of tasks simultaneously, enhancing the multifunctionality of optical information processing systems.

The other authors include graduate student researchers Jingxi Li, Yi-Chun Hung, Deniz Mengu and postdoc scholar Dr. Onur Kulce, all from UCLA School of Engineering and CNSI. The researchers acknowledge the funding of the US Air Force Office of Scientific Research (AFOSR).

Article: https://doi.org/10.1038/s41377-022-00849-x