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Primeur weekly 2021-01-11

Exascale supercomputing

Preparing an earthquake risk assessment application for exascale ...

Quantum computing

A bit too much: reducing the bit width of Ising models for quantum annealing ...

The world's first integrated quantum communication network ...

Focus on Europe

GBP 20 million funding boost for science supercomputer will drive science simulation and UK-wide innovation ...

Northern Data acquires data centre site in Northern Sweden fully powered by green energy ...

Research and Markets to issue report on High Performance Computing (HPC) Market by Component, Deployment Type, Organization Size, Server Prices Band, Application Area, and Region - Global Forecast to 2025 ...

Environmental researchers benefit from powerful supercomputer at Plymouth Marine Laboratory ...

Middleware

XSEDE welcomes new service providers ...

Hardware

IBM appoints Gary D. Cohn as Vice Chairman ...

IBM appoints Martin Schroeter as CEO of "NewCo" independent managed infrastructure services business to spin out from IBM ...

Light-based processors boost machine-learning processing ...

E4 Computer Engineering announces University of Pisa as the first customer of Ultrafast Storage, Totally Integrated (USTI), the new solution for high performance distributed block storage ...

Existing Northern Data bitcoin mining customer expands contract volume by more than 200 MW ...

Power XL Pro launched as new professional server based on AMD EPYC technology ...

Swinburne-led research team demonstrates world's fastest optical neuromorphic processor ...

Applications

Supercomputer models describe chloride's role in corrosion ...

HPC-AI Advisory Council to host HPC AI AC Conference in Japan on January, 26 ...

Insights through atomic simulation ...

Advanced materials in a snap ...

New data-driven global climate model provides projections for urban environments ...

Frequency data for stable power supply ...

Physicists observe competition between magnetic orders ...

UTSA Artificial Intelligence Consortium receives over $1 million in research funding ...

Entangling electrons with heat ...

NIO partners with NVIDIA to develop a new generation of automated driving electric vehicles ...

Navantia leverages Ansys' digital transformation solutions to design next-gen naval vessels ...

Engineering graduate student places second in international research competition ...

The Cloud

Covid-19 genome sequencing project gets major upgrade ...

IBM provides Harris-Stowe State University with $2 million in Artificial Intelligence and open hybrid Cloud technology resources to help students build modern skills ...

IBM and Avertra collaborate to drive digital transformation for energy & utilities clients with IBM Cloud ...

UJET CCaaS Cloud contact centre now available on Oracle Cloud Marketplace ...

Light-based processors boost machine-learning processing


Schematic representation of a processor for matrix multiplications which runs on light. Credit: University of Oxford.
6 Jan 2021 Lausanne, Münster - An international team of scientists have developed a photonic processor that uses rays of light inside silicon chips to process information much faster than conventional electronic chips. Published inNature, the breakthrough study was carried out by scientists from the Ecole Polytechnique Fédérale de Lausanne (EPFL), the Universities of Oxford, Münster, Exeter, Pittsburgh, and IBM Research - Zurich.
Schematic representation of a processor for matrix multiplications which runs on light. Together with an optical frequency comb, the waveguide crossbar array permits highly parallel data processing. Credit: WWU/AG Pernice.

The exponential growth of data traffic in our digital age poses some real challenges on processing power. And with the advent of machine learning and AI in, for example, self-driving vehicles and speech recognition, the upward trend is set to continue. All this places a heavy burden on the ability of current computer processors to keep up with demand.

Now, an international team of scientists has turned to light to tackle the problem. The researchers developed a new approach and architecture that combines processing and data storage onto a single chip by using light-based, or "photonic" processors, which are shown to surpass conventional electronic chips by processing information much more rapidly and in parallel.

The scientists developed a hardware accelerator for so-called matrix-vector multiplications, which are the backbone of neural networks - algorithms that simulate the human brain, which themselves are used for machine-learning algorithms. Since different light wavelengths (colours) don't interfere with each other, the researchers could use multiple wavelengths of light for parallel calculations. But to do this, they used another innovative technology, developed at EPFL, a chip-based "frequency comb", as a light source.

"Our study is the first to apply frequency combs in the field of artificially neural networks", stated Professor Tobias Kippenberg at EPFL, one the study's leads. Professor Kippenberg's research has pioneered the development of frequency combs. "The frequency comb provides a variety of optical wavelengths that are processed independently of one another in the same photonic chip."

"Light-based processors for speeding up tasks in the field of machine learning enable complex mathematical tasks to be processed at high speeds and throughputs", stated senior co-author Wolfram Pernice at Münster University, one of the professors who led the research. "This is much faster than conventional chips which rely on electronic data transfer, such as graphic cards or specialized hardware like Tensor Processing Units (TPUs)."

After designing and fabricating the photonic chips, the researchers tested them on a neural network that recognizes of hand-written numbers. Inspired by biology, these networks are a concept in the field of machine learning and are used primarily in the processing of image or audio data. "The convolution operation between input data and one or more filters - which can identify edges in an image, for example, are well suited to our matrix architecture", stated Johannes Feldmann, now based at the University of Oxford Department of Materials. Nathan Youngblood, Oxford University, added: "Exploiting wavelength multiplexing permits higher data rates and computing densities, i.e. operations per area of processer, not previously attained."

The team of researchers led by Prof. Wolfram Pernice from the Institute of Physics and the Center for Soft Nanoscience at the University of Münster implemented a hardware accelerator for so-called matrix multiplications, which represent the main processing load in the computation of neural networks. Neural networks are a series of algorithms which simulate the human brain. This is helpful, for example, for classifying objects in images and for speech recognition.

The researchers combined the photonic structures with phase-change materials (PCMs) as energy-efficient storage elements. PCMs are usually used with DVDs or BluRay discs in optical data storage. In the new processor this makes it possible to store and preserve the matrix elements without the need for an energy supply. To carry out matrix multiplications on multiple data sets in parallel, the Münster physicists used a chip-based frequency comb as a light source. A frequency comb provides a variety of optical wavelengths which are processed independently of one another in the same photonic chip. As a result, this enables highly parallel data processing by calculating on all wavelengths simultaneously - also known as wavelength multiplexing. "Our study is the first one to apply frequency combs in the field of artificially neural networks", stated Wolfram Pernice.

In the experiment the physicists used a so-called convolutional neural network for the recognition of handwritten numbers. These networks are a concept in the field of machine learning inspired by biological processes. They are used primarily in the processing of image or audio data, as they currently achieve the highest accuracies of classification. "The convolutional operation between input data and one or more filters - which can be a highlighting of edges in a photo, for example - can be transferred very well to our matrix architecture", explained Johannes Feldmann, the lead author of the study.

"Exploiting light for signal transference enables the processor to perform parallel data processing through wavelength multiplexing, which leads to a higher computing density and many matrix multiplications being carried out in just one timestep. In contrast to traditional electronics, which usually work in the low GHz range, optical modulation speeds can be achieved with speeds up to the 50 to 100 GHz range." This means that the process permits data rates and computing densities, i.e. operations per area of processor, never previously attained.

"This work is a real showcase of European collaborative research", stated David Wright at the University of Exeter, who leads the EU project FunComp, which funded the work. "Whilst every research group involved is world-leading in their own way, it was bringing all these parts together that made this work truly possible."

The study is published inNature, and has far-reaching applications: higher simultaneous - and energy-saving - processing of data in artificial intelligence, larger neural networks for more accurate forecasts and more precise data analysis, large amounts of clinical data for diagnoses, enhancing rapid evaluation of sensor data in self-driving vehicles, and expanding Cloud computing infrastructures with more storage space, computing power, and applications software.

J. Feldmann, N. Youngblood, M. Karpov, H. Gehring, X. Li, M. Stappers, M. Le Gallo, X. Fu, A. Lukashchuk, A.S. Raja, J. Liu, C.D. Wright, A. Sebastian, T.J. Kippenberg, W.H.P. Pernice, and H. Bhaskaran are the authors of the paper titled " Parallel convolution processing using an integrated photonic tensor core ", published inNature, 7 January 2021 - DOI:x 10.1038/s41586-020-03070-1.
Source: Ecole Polytechnique Fédérale de Lausanne - EPFL, University of Münster

Back to Table of contents

Primeur weekly 2021-01-11

Exascale supercomputing

Preparing an earthquake risk assessment application for exascale ...

Quantum computing

A bit too much: reducing the bit width of Ising models for quantum annealing ...

The world's first integrated quantum communication network ...

Focus on Europe

GBP 20 million funding boost for science supercomputer will drive science simulation and UK-wide innovation ...

Northern Data acquires data centre site in Northern Sweden fully powered by green energy ...

Research and Markets to issue report on High Performance Computing (HPC) Market by Component, Deployment Type, Organization Size, Server Prices Band, Application Area, and Region - Global Forecast to 2025 ...

Environmental researchers benefit from powerful supercomputer at Plymouth Marine Laboratory ...

Middleware

XSEDE welcomes new service providers ...

Hardware

IBM appoints Gary D. Cohn as Vice Chairman ...

IBM appoints Martin Schroeter as CEO of "NewCo" independent managed infrastructure services business to spin out from IBM ...

Light-based processors boost machine-learning processing ...

E4 Computer Engineering announces University of Pisa as the first customer of Ultrafast Storage, Totally Integrated (USTI), the new solution for high performance distributed block storage ...

Existing Northern Data bitcoin mining customer expands contract volume by more than 200 MW ...

Power XL Pro launched as new professional server based on AMD EPYC technology ...

Swinburne-led research team demonstrates world's fastest optical neuromorphic processor ...

Applications

Supercomputer models describe chloride's role in corrosion ...

HPC-AI Advisory Council to host HPC AI AC Conference in Japan on January, 26 ...

Insights through atomic simulation ...

Advanced materials in a snap ...

New data-driven global climate model provides projections for urban environments ...

Frequency data for stable power supply ...

Physicists observe competition between magnetic orders ...

UTSA Artificial Intelligence Consortium receives over $1 million in research funding ...

Entangling electrons with heat ...

NIO partners with NVIDIA to develop a new generation of automated driving electric vehicles ...

Navantia leverages Ansys' digital transformation solutions to design next-gen naval vessels ...

Engineering graduate student places second in international research competition ...

The Cloud

Covid-19 genome sequencing project gets major upgrade ...

IBM provides Harris-Stowe State University with $2 million in Artificial Intelligence and open hybrid Cloud technology resources to help students build modern skills ...

IBM and Avertra collaborate to drive digital transformation for energy & utilities clients with IBM Cloud ...

UJET CCaaS Cloud contact centre now available on Oracle Cloud Marketplace ...