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Teaching photonic chips to learn

28.11.2022 - A novel hardware will speed up the training of machine learning systems.

Before a machine can perform intelli­gence tasks such as recognizing the details of an image, it must be trained. Training of modern-day artificial intelli­gence (AI) systems like Tesla’s autopilot costs several million dollars in electric power consumption and requires super­computer-like infra­structure. This surging AI appetite leaves an ever-widening gap between computer hardware and demand for AI. Photonic integrated circuits – optical chips – have emerged as a possible solution to deliver higher computing performance, as measured by the number of operations performed per second per watt used. However, though they’ve demons­trated improved core operations in machine intelligence used for data classi­fication, photonic chips have yet to improve the actual front-end learning and machine training process. 

Machine learning is a two-step procedure. First, data is used to train the system and then other data is used to test the performance of the AI system. Now, a team of researchers from the George Washington Univer­sity, Queens University, University of British Columbia and Princeton University set out to do just that. After one training step, the team observed an error and recon­figured the hardware for a second training cycle followed by additional training cycles until a suffi­cient AI perfor­mance was reached – e.g. the system is able to correctly label objects appearing in a movie. Thus far, photonic chips have only demons­trated an ability to classify and infer information from data. Now, researchers have made it possible to speed up the training step itself.

This added AI capability is part of a larger effort around photonic tensor cores and other electronic-photonic appli­cation-specific integrated circuits (ASIC) that leverage photonic chip manu­facturing for machine learning and AI appli­cations. “This novel hardware will speed up the training of machine learning systems and harness the best of what both photonics and electronic chips have to offer. It is a major leap forward for AI hardware acceleration. These are the kinds of advancements we need in the semi­conductor industry as underscored by the recently passed CHIPS Act”, Volker Sorger from the George Washington University said. “The training of AI systems costs a signi­ficant amount of energy and carbon footprint. For example, a single AI trans­former takes about five times as much CO2 in elec­tricity as a gasoline car spends in its lifetime. Our training on photonic chips will help to reduce this overhead”, Bhavin Shastri from Queens University added. (Source: GWU)

Reference: M. J. Filipovich et al.: Silicon photonic architecture for training deep neural networks with direct feedback alignment, Optica 9, 1323 (2022); DOI: 10.1364/OPTICA.475493

Link: Devices & Intelligent Systems Laboratory, Dept. of Electrical and Computer Engineering, George Washington University, Washington DC, USA

Further Reading: A. Gonzalez & D. Costa: Programmable chips for 5G applications, PhotonicsViews 19, 38 (2022); DOI: 10.1002/phvs.202200002

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Welding with Civan's Ultrafast CBC-Laser: Basics, Opportunities and Challenges

The first part of the webinar will provide an overview of the fundamentals and challenges of the welding process and the features of the CIVAN CBC laser. The second part of the webinar will discuss approaches to take advantage of fast, arbitrary beam shaping to control process problems.

Register now

Digital tools or software can ease your life as a photonics professional by either helping you with your system design or during the manufacturing process or when purchasing components. Check out our compilation:

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