The refractive processor designed with deep learning computes hundreds of transformations in parallel

The refractive processor designed with deep learning computes hundreds of transformations in parallel

Massively parallel global linear transformations using a deep multi-wavelength diffraction neural network. Credit: Ozcan Research Group, UCLA. In today’s digital age, computational tasks are becoming increasingly complex. This, in turn, has led to an exponential growth in the power consumed by digital computers. Thus, it is necessary to develop hardware resources that can perform large-scale …

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