  Optical approaches have made great strides towards the goal of high-speed,
energy-efficient computing necessary for modern deep learning and AI
applications. Read-in and read-out of data, however, limit the overall
performance of existing approaches. This study introduces a multilayer
optoelectronic computing framework that alternates between optical and
optoelectronic layers to implement matrix-vector multiplications and rectified
linear functions, respectively. Our framework is designed for real-time,
parallelized operations, leveraging 2D arrays of LEDs and photodetectors
connected via independent analog electronics. We experimentally demonstrate
this approach using a system with a three-layer network with two hidden layers
and operate it to recognize images from the MNIST database with a recognition
accuracy of 92% and classify classes from a nonlinear spiral data with 86%
accuracy. By implementing multiple layers of a deep neural network
simultaneously, our approach significantly reduces the number of read-ins and
read-outs required and paves the way for scalable optical accelerators
requiring ultra low energy.
