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Optical system uses diffractive processors to achieve large-scale nonlinear computation
Researchers at the University of California, Los Angeles (UCLA) have developed an optical computing framework that performs ...
A new technical paper titled “Massively parallel and universal approximation of nonlinear functions using diffractive ...
Dr. James McCaffrey of Microsoft Research presents the second of four machine learning articles that detail a complete end-to-end production-quality example of neural regression using PyTorch. The ...
Neural network training could one day require less computing power and hardware, thanks to a new nanodevice that can run neural network computations using 100 to 1000 times less energy and area than ...
Researchers at the University of California, Los Angeles (UCLA) have developed an optical computing framework that performs large-scale nonlinear ...
It’s been ten years since AlexNet, a deep learning convolutional neural network (CNN) model running on GPUs, displaced more traditional vision processing algorithms to win the ImageNet Large Scale ...
UCLA researchers demonstrate diffractive optical processors as universal nonlinear function approximators using linear ...
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