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.

光学方法朝着现代深度学习和 AI 应用所需的高速、节能计算目标取得了长足的进步。但是，数据的读入和读出限制了现有方法的整体性能。本研究介绍了一个多层光电计算框架，该框架在光学层和光电层之间交替，以分别实现矩阵向量乘法和修正线性函数。我们的框架专为实时、并行操作而设计，利用 LED 的 2D 阵列和通过独立模拟电子设备连接的光电探测器。我们使用一个具有两个隐藏层的三层网络的系统实验演示了这种方法，并操作它以 92% 的识别准确率识别 MNIST 数据库中的图像，并以 86% 的准确率从非线性螺旋数据中分类类别。通过同时实现多层深度神经网络，我们的方法显著减少了所需的读入和读出次数，并为需要超低功耗的可扩展光加速器铺平了道路。