Faster, energy-efficient diffractive ONN could be used for image and video processing. For the first time, researchers have harnessed the power of a surface normal nonlinear photodetector (SNPD) to improve the speed and energy efficiency of a diffractive optical neural network (ONN). This innovat
Researchers have utilized an SNPD to enhance the speed and efficiency of optical neural networks, showcasing a potential 1000-fold increase in processing speeds compared to traditional camera sensors. This advancement heralds a new era for AI-driven vision systems.For the first time, researchers have harnessed the power of a surface normal nonlinear photodetector to improve the speed and energy efficiency of a diffractive optical neural network .
“Artificial intelligence is playing increasingly important roles in our lives. From large language models to autonomous cars, we see significant improvements enabled by AI,” explains Ashtiani. “Neural networks, inspired by how the human brain learns and performs various tasks, are at the heart of the progress in AI. One of the widespread applications of neural networks is recognizing patterns and objects, which gives vision to machines.
“However, since the images are originally in the optical domain , it can be faster and more energy efficient to process them optically using ONN. Among different technologies, ONNs based on spatial light modulators enable the optical processing of high-resolution images and videos. This processing requires nonlinear modules and camera sensors are conventionally used to introduce this needed nonlinearity, which takes several milliseconds.
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