FlyCast

Solid-state 360° optical beamforming for reconfigurable multicast optical wireless communications

Optical wireless communication is an attractive technique for data center interconnects due to its low latency line-of-sight connectivity. Multicast, on the other hand, is an important data center network function that can improve traffic throughput, reduce latency, and make efficient use of network resources. To enable reconfigurable multicast in data center optical wireless networks, we propose a novel 360° optical beamforming scheme based on the principle of superposition of orbital angular momentum modes, emitting beams from the source rack pointing towards any combination of other racks so that connections are established between the source and multiple destination racks. We experimentally demonstrate the scheme using solid state devices for a scenario where racks are arranged in a hexagonal formation in which a source rack can connect with any number of adjacent racks simultaneously, with each link transmitting 70 Gb/s on-off-keying modulations at bit error rates of <10^-6 at 1.5-m and 2.0-m link distances.

Freely scalable and reconfigurable optical hardware for deep learning

As deep neural network (DNN) models grow ever-larger, they can achieve higher accuracy and solve more complex problems. This trend has been enabled by an increase in available compute power; however, efforts to continue to scale electronic processors are impeded by the costs of communication, thermal management, power delivery and clocking. To improve scalability, we propose a digital optical neural network (DONN) with intralayer optical interconnects and reconfigurable input values. The path-length-independence of optical energy consumption enables information locality between a transmitter and a large number of arbitrarily arranged receivers, which allows greater flexibility in architecture design to circumvent scaling limitations. In a proof-of-concept experiment, we demonstrate optical multicast in the classification of 500 MNIST images with a 3-layer, fully-connected network. We also analyze the energy consumption of the DONN and find that digital optical data transfer is beneficial over electronics when the spacing of computational units is on the order of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$>10\,\upmu $$\end{document}>10μm.