The widespread adoption of machine learning (ML) tools deployed at large scale has created an interest in new hardware for ML. LightOn, a French start up based in Paris, has created an optical coprocessor, the OPU (optical processing unit), that can accelerate such computations at large scale while using less power. The input of the OPU must be binary. It is hard to design a binary encoder that works well on natural data, so we investigate if is possible to learn it from them, and to transfer the encoder that we learned to other datasets. The aim of this work is to explore a deep learning model called autoencoder (AE) to build a binary AE skim which is cheap, scale invariant and input independent.