
Increasing demand for high-performance AI has engendered interest in using photonic processing instead of conventional electronic processing for AI computations. Optical computing has the potential to boost AI’s computational throughput, processing speed, and energy efficiency by orders of magnitude.
But first, optical neural networks must achieve recognition accuracy that is on par with electronic neural networks. A nanophotonic neural network, developed by researchers at the University of Washington and Princeton University, aims to overcome this limitation.
The researchers embedded parallelized optical computation into flat camera optics 4 mm in length. The camera performs neural network computations during image...
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