Diffractive Optical Processor Computes Hundreds of Linear Transformations in Parallel
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LOS ANGELES - Californer -- Computing using light can potentially reduce latency and power consumption, benefiting from the parallelism that optical systems have. For example, a single optical processor can execute many distinct computational tasks simultaneously, such as the parallel computing of different transformations, which could be valuable for numerous applications, including the acceleration of machine learning-based inference.

In their new paper, published in Advanced Photonics, researchers from the University of California, Los Angeles (UCLA) reported a wavelength-multiplexed diffractive optical processor that enables simultaneous computation of hundreds of different complex-valued linear transformations through different wavelength channels. Designed using deep learning, this diffractive optical processor consists of structured diffractive surfaces, made of passive transmissive materials. In this optical processor, a pre-determined group of discrete wavelengths encodes the input and output information. Each wavelength is dedicated to a unique target function or complex-valued linear transformation. Following the deep learning-based design phase, this processor can be fabricated using 3D printing or photolithography and then assembled to physically form an optical processor, which can simultaneously perform a large group of target transformations between its input and output. These target transforms can be specifically assigned for distinct functions, including, for example, image classification, segmentation, encryption, and reconstruction, or they can be dedicated to computing different convolutional filter operations or fully connected layers in a neural network. All these linear transforms or desired functions are executed simultaneously at the speed of light, where each function is assigned to a unique wavelength, allowing the broadband optical processor to compute with extreme throughput and parallelism.

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This broadband diffractive processor design does not require any wavelength-selective elements such as spectral or color filters, and is compatible with a wide range of materials with different dispersion properties. The UCLA researchers believe that this technology can be used to develop high-performance optical processors that operate at different parts of the electromagnetic spectrum, including the visible and infrared wavelengths. In addition, due to its capability to directly process the input spectral information, the reported framework will also inspire the development of multicolor and hyperspectral machine vision systems that perform statistical inference based on the spatial and spectral information of the input objects, with applications in biomedical imaging and sensing for the detection and specific imaging of substances with unique spectral characteristics.

This research was led by Professor Aydogan Ozcan, the Volgenau Chair for Engineering Innovation at the Electrical and Computer Engineering (ECE) department at UCLA, along with Professor Mona Jarrahi, the Northrop Grumman Endowed Chair at UCLA ECE. The other authors of this work include graduate students Jingxi Li, Tianyi Gan, Bijie Bai and Yi Luo, all from UCLA School of Engineering. Researchers acknowledge the funding support of the US Air Force Office of Scientific Research (AFOSR).

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Publication: https://doi.org/10.1117/1.AP.5.1.016003

Source: UCLA ITA
Filed Under: Science
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