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Optalysys Makes Deep Learning Breakthrough

Photonics.com
Apr 2018
YORKSHIRE, England, April 6, 2018 — Optical computing technology developer Optalysys Ltd. has built a convolutional neural network using optical processing technology.

Convolutional neural networks (CNNs) are expanding areas of machine learning image recognition and analysis used in pivotal applications, such as autonomous vehicles, weather forecasting, and medical image analysis. These models are computationally extremely intensive, particularly for complex models with many convolutional layers to process. The use of CNNs has advanced in recent years largely through the advancement of graphics processors. However, even though these offer considerable advantages over conventional processors, they are limited by the breakdown of Moore’s law and the high energy costs, which can run into several millions of dollars for top-end supercomputers.

Optalysys’s optical processing technology uses a fundamentally different approach, with energy-efficient laser light rather than silicon as the processing medium. This delivers speed improvements of several orders of magnitude over conventional computing at a fraction of the energy consumption.

“This is a hugely significant leap forward for the field of AI and clearly demonstrates the global potential for our enabling technology,” said Nick New, founder and CEO of Optalysys. “Optalysys has, for the first time ever, applied optical processing to the highly complex and computationally demanding area of CNNs with initial accuracy rates of over 70 percent. Through our uniquely scalable and highly efficient optical approach, we are developing models that will offer whole new levels of capability, not only cloud-based, but also opening up the extraordinary potential of CNNs to mobile systems.”

Optalysys is a developer of optical computing platforms to enable a new levels of processing capability.

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