Deep physical neural networks trained with backpropagation

Deep-learning models have become pervasive tools in science and engineering. However, their energy requirements now increasingly limit their scalability. Deep-learning accelerators aim to perform deep learning energy-efficiently, usually targeting the inference phase and often by exploiting physical substrates beyond conventional electronics. Approaches so far have been unable to apply the backpropagation algorithm to train unconventional novel hardware in situ. The advantages of backpropagation have made it the de facto training method for large-scale neural networks, so this deficiency constitutes a major impediment.

Physics of Quantum Electronics (PQE)-2022 Conference Honors NTT Research PHI Lab Director Yoshihisa Yamamoto with Willis E. Lamb Award

Physics of Quantum Electronics (PQE) Conference has named Yoshihisa Yamamoto, NTT Research Physics & Informatics (PHI) Lab Director and Emeritus Professor of Applied Physics and Electrical Engineering at Stanford University, one of the three winners of the Willis E. Lamb Award for Laser Science and Quantum Optics. The award was presented on January 12 at the PQE-2022 Winter Colloquium in Snowbird, Utah.

Physical Systems Perform Machine-Learning Computations

You may not be able to teach an old dog new tricks, but Cornell researchers have found a way to train physical systems, ranging from computer speakers and lasers to simple electronic circuits, to perform machine-learning computations, such as identifying handwritten numbers and spoken vowel sounds.

The CyberWire Daily Briefing

NTT Scientists Advance Post-Quantum Cryptography at FOCS Symposium Two scientists from the NTT Research Lab and NTT Social Informatics Laboratories will present papers on post-quantum cryptography at FOCS event.

‘Digital twins’ manage the airport and hospitals. Are people next?

San Francisco is living a double life, where taxpayer dollars are saved, disasters are averted, massive structures are built and lives are saved. “Digital twins” are highly detailed three-dimensional models run by computer programs that are being used to help run hospital buildings and parts of the airport, and to replicate the human heart, putting San Francisco at the forefront of the technology, experts say. All the data available about a real-world building or vehicle is entered into a computer program that replicates the original in three dimensions on a screen.

NEW TECH: How a ‘bio digital twin’ that helps stop fatal heart attacks could revolutionize medicine

Without much fanfare, digital twins have established themselves as key cogs of modern technology. A digital twin is a virtual duplicate of a physical entity or a process — created by extrapolating data collected from live settings. Digital twins enable simulations to be run without risking harm to the physical entity; they help inform efficiency gains made in factories and assure the reliability of jet engines, for instance.

NTT Research PHI Lab Adds To Its Scientific Staff

NTT Research, Inc., a division of NTT announced that in the first eight months of 2021, its Physics & Informatics (PHI) Lab has gained six new scientists. These include Senior Research Scientists Adil Gangat and Sho Sugiura; Research Scientists Thibault Chervy, Edwin Ng, and Gautam Reddy; and Post-doctoral Fellow Yonghwi Kim. These additions bring the total number of PHI Lab scientists to 18, including PHI Lab Director Yoshihisa Yamamoto, generating further momentum to this group as NTT Research begins its third year of operations:

Sign up for our newsletter and receive the latest news from NTT Research: