Accelerate Distributed Deep Learning with a Fast Reconfigurable Optical Network

Abstract

We propose a fast-reconfigurable and scalable optical network architecture, which employs a flow-based transmit scheduling scheme to accelerate data parallelism in distributed deep learning. Experimental results demonstrate that the 4-node prototype achieves training times comparable to those of ideal electrical switching.