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January 13, 2021 Wednesday 03:32:02 PM IST

Light-carrying chips advance machine learning

International Edu News

A team of international scientists has demonstrated an initial prototype of a photonic processor using tiny rays of light confined inside silicon chips that can process information much more rapidly than electronic chips and also in parallel - something traditional chips are incapable of doing. The collaborative discovery, by researchers at the Universities of Oxford, Münster, Exeter, Pittsburgh, École Polytechnique Fédérale (EPFL) and IBM Research Europe, has been published in Nature.

Machine learning and artificial intelligence applications make use of vast troves of data. Our data is increasing exponentially and using this data to create information requires computer processing. The capabilities of conventional computer processors are not sufficient to keep up with this demand. This international research team has developed a new approach and processor architecture that provides a potential avenue to perform these tasks at a high throughput – essentially by combining processing and data storage functionalities onto a single chip – so called in-memory processors, but using light.

The team implemented a hardware accelerator for so-called matrix-vector multiplications. Such operations form the backbone of neural networks (a series of algorithms that simulate the human brain) that are used to compute machine learning algorithms. Using light allowed the team to use multiple wavelengths of light to do parallel calculations since light has the amazing property of having different colours that do not interfere. However, to do this, they used yet another recent invention, a chip-based frequency comb, as a light source.

Once the chips were designed and fabricated, the researchers used a convolution neural network for the recognition of handwritten numbers. These networks are a concept in the field of machine learning inspired by biological processes. Used primarily in the processing of image or audio data, they currently achieve the highest accuracies for classification.

The results have a wide range of potential applications:
    In the field of artificial intelligence more, data can be processed simultaneously while saving energy.
    The use of larger neural networks allows more accurate, and hitherto unattainable, forecasts, and more precise data analysis.
    Within clinical settings, the photonic processors can support the evaluation of large quantities of data for diagnoses, e.g. high-resolution 3D data produced in special imaging methods.
    In the fields of self-driving vehicles, rapid evaluation of sensor data can be enhanced
    IT infrastructures such as cloud computing can be expanded by providing more storage space, computing power, and application software. 

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