Optical neural network accelerator for machine learning: Page 2 of 2

December 29, 2020 // By Jean-Pierre Joosting
Optical neural network accelerator for machine learning
Reseachers develop an optical convolutional neural network accelerator capable of processing large amounts of information, on the order of petabytes, per second.

According to Volker Sorger, associate professor of electrical and computer engineering at the George Washington University, ”This massively parallel amplitude-only Fourier optical processor is heralding a new era for information processing and machine learning. We show that training this neural network can account for the lack of phase information"

Puneet Gupta, professor and vice chair of computer engineering at UCLA commented, ”Optics allows for processing large-scale matrices in a single time-step, which allows for new scaling vectors of performing convolutions optically."

This innovation, which harnesses the massive parallelism of light, heralds a new era of optical signal processing for machine learning with numerous applications, including in self-driving cars, 5G networks, data-centers, biomedical diagnostics, data-security and more.

Hamed Dalir, Co-founder, Optelligence LLC says, ”This prototype demonstration shows a commercial path for optical accelerators ready for a number of applications like network-edge processing, data-centers and high-performance compute systems."

The paper, "Massively Parallel Amplitude-Only Fourier Neural Network" was published in the journal OPTICA.



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