Functional composition and applicability of microcircuits of imported TVs

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Functional composition and applicability of microcircuits of imported TVs (reference book 875 Kb)

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Optical Neural Network Accelerator 09.02.2021

Researchers from the George Washington University (USA) and the University of California, together with specialists from a startup in the field of photonics Optelligence LLC, have created a special optical accelerator for a convolutional neural network. It is a photonics based solution based on the unique scaling laws in optics.

The non-iterative timing of the processor, combined with fast programmability and massive parallelism, allows an optical machine learning system to outperform even the most modern graphics chipsets by more than an order of magnitude. And in the future, the new system can be further optimized.

Existing electronic machine learning hardware processes information sequentially. And the new optical processor uses Fourier optics, a frequency filtering concept that allows you to perform the necessary neural network convolution operations, as well as simpler element-wise multiplications using digital mirror technology.

The new processor can process large-scale matrices in a single time step, allowing new scaling vectors to be used to perform optical convolutions. And this already has great potential for the development of machine learning.

The prototype processes large amounts of information - on the order of a petabyte per second - and therefore opens up opportunities for creating revolutionary photonic machines of the future. The development can be applied in unmanned vehicles, 5G networks, data centers, biomedical diagnostics, data security and others.

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