Taichi: A large-scale diffractive hybrid photonic AI chiplet
A combined team of engineers from Tsinghua University and the Beijing National Research Center for Information Science and Technology, both in China, has developed a large-scale diffractive hybrid photonic AI chiplet for use in high-efficiency artificial general intelligence applications. Their paper is published in the journal Science.
Optical computing has emerged as a powerful approach for high-speed and energy-efficient information processing. Diffractive optical networks, in particular, enable large-scale parallel computation through the use of passive structured phase masks and the propagation of light. However, one major challenge remains: systems trained in model-based simulations often fail to perform…
Penn Engineers have developed the first programmable chip that can train nonlinear neural networks using light—a breakthrough that could dramatically speed up AI training, reduce energy use and even pave the way for fully light-powered computers.
Aalto University researchers have developed a method to execute AI tensor operations using just one pass of light. By encoding data directly into light waves, they enable calculations to occur naturally and simultaneously. The approach works passively, without electronics, and could soon be integrated into photonic chips. If adopted, it…