Optical processor for a binarized neural network

We propose and experimentally demonstrate an optical processor for a binarized neural network (NN). Implementation of a binarized NN involves multiply-accumulate operations, in which positive and negative weights should be implemented. In the proposed processor, the positive and negative weights are realized by switching the operations of a dual-drive Mach-Zehnder modulator (DD-MZM) between two quadrature points corresponding to two binary weights of +1 and -1, and the multiplication is also performed at the DD-MZM. The accumulation operation is realized by dispersion-induced time delays and detection at a photodetector (PD). A proof-of-concept experiment is performed. A binarized convolutional neural network (CNN) accelerated by the optical processor at a speed of 32 giga floating point operations/s (GFLOPS) is tested on two benchmark image classification tasks. The large bandwidth and parallel processing capability of the processor has high potential for next generation data computing. (C) 2022 Optica Publishing Group

Automated summary

We propose and experimentally demonstrate an optical processor for a binarized neural network, which is capable of implementing positive and negative weights as well as multiply-accumulate operations. The accumulation operation is achieved through dispersion-induced time delays and detection at a photodetector. A proof-of-concept experiment shows the high speed and large bandwidth parallel processing capability of the processor in binarized convolutional neural network tasks.