March 14, 2022

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 […]

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

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. https://techilive.in/physical-systems-perform-machine-learning-computations/