Binarization for optical processing units via REINFORCE

Optical Processing Units (OPUs) are computing devices which perform random projections of input vectors by exploiting the physical phenomenon of scattering a light source through an opaque medium. OPUs have successfully been proposed to carry out approximate kernel ridge regression at scale and with low power consumption by the means of optical random features. OPUs require input vectors to be binary, and this work proposes a novel way to perform supervised data binarization. The main difficulty to develop a solution is that the OPU projection matrices are unknown which poses a challenge in deriving a binarization approach in an end-to-end fashion. Our approach is based on the REINFORCE gradient estimator, which allows us to estimate the gradient of the loss function with respect to binarization parameters by treating the OPU as a black-box. Through experiments on several UCI classification and regression problems, we show that our method outperforms alternative unsupervised and supervised binarization techniques.