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Thursday, March 22, 2018

First optical AI system built

By Nick Flaherty www.flaherty.co.uk

Optalysys in Yorkshire, UK, has successfully built the world’s first implementation of a Convolutional Neural Network using their Optical Processing Technology.

Convolutional Neural Networks, or CNNs, are used for image and pattern recognition in applications such as Autonomous Vehicles, Weather Forecasting and Medical Image Analysis. These models are computationally extremely intensive, particularly for complex models, where there can be many convolutional “layers” to process.

While GPUs offer considerable advantages over conventional processors, they are limited by the breakdown of Moore’s Law and energy usage.

Optalysys’s optical processing technology is a fundamentally different approach using 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 Dr. 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%. 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.”

The demonstration successfully shows the Optalysys Optical Processing Technology processing a CNN, using the popular MNIST data set of hand-drawn numerals, which contains 60,000 training characters and 10,000 testing characters.

The Optical Computing Platforms uses a coprocessor based on an established diffractive optical approach that uses the photons of low-power laser light instead of conventional electricity and its elctrons. This inherently parallel technology is highly scalable for CNNs architectures.



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