论文标题
浅色的循环：使用光子学的协调员进行神经网络的可扩展培训
Light-in-the-loop: using a photonics co-processor for scalable training of neural networks
论文作者
论文摘要
随着神经网络变得更大，更复杂和渴望数据，培训成本飙升。尤其是当需要终身学习时，例如在推荐系统或自动驾驶汽车中，这可能很快就会变得不可持续。在这项研究中，我们提出了第一个能够加速数字化神经网络的训练阶段的光学协调员。我们依靠直接反馈对准作为反向传播的替代方案，并以光学执行错误投影步骤。利用我们的协同处理器提供的光随机预测，我们证明了它用于训练神经网络以识别手写数字。
As neural networks grow larger and more complex and data-hungry, training costs are skyrocketing. Especially when lifelong learning is necessary, such as in recommender systems or self-driving cars, this might soon become unsustainable. In this study, we present the first optical co-processor able to accelerate the training phase of digitally-implemented neural networks. We rely on direct feedback alignment as an alternative to backpropagation, and perform the error projection step optically. Leveraging the optical random projections delivered by our co-processor, we demonstrate its use to train a neural network for handwritten digits recognition.
