Xu Shaofu, Wang Jing, Shu Haowen, Zhang Zhike, Yi Sicheng, Bai Bowen, Wang Xingjun, Liu Jianguo, Zou Weiwen
State Key Laboratory of Advanced Optical Communication Systems and Networks, Intelligent Microwave Lightwave Integration Innovation Center (imLic), Department of Electronic Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 200240, China.
State Key Laboratory of Advanced Optical Communications System and Networks, Department of Electronics, School of Electronics Engineering and Computer Science, Peking University, Beijing, 100871, China.
Light Sci Appl. 2021 Nov 1;10(1):221. doi: 10.1038/s41377-021-00666-8.
Optical implementations of neural networks (ONNs) herald the next-generation high-speed and energy-efficient deep learning computing by harnessing the technical advantages of large bandwidth and high parallelism of optics. However, due to the problems of the incomplete numerical domain, limited hardware scale, or inadequate numerical accuracy, the majority of existing ONNs were studied for basic classification tasks. Given that regression is a fundamental form of deep learning and accounts for a large part of current artificial intelligence applications, it is necessary to master deep learning regression for further development and deployment of ONNs. Here, we demonstrate a silicon-based optical coherent dot-product chip (OCDC) capable of completing deep learning regression tasks. The OCDC adopts optical fields to carry out operations in the complete real-value domain instead of in only the positive domain. Via reusing, a single chip conducts matrix multiplications and convolutions in neural networks of any complexity. Also, hardware deviations are compensated via in-situ backpropagation control provided the simplicity of chip architecture. Therefore, the OCDC meets the requirements for sophisticated regression tasks and we successfully demonstrate a representative neural network, the AUTOMAP (a cutting-edge neural network model for image reconstruction). The quality of reconstructed images by the OCDC and a 32-bit digital computer is comparable. To the best of our knowledge, there is no precedent of performing such state-of-the-art regression tasks on ONN chips. It is anticipated that the OCDC can promote the novel accomplishment of ONNs in modern AI applications including autonomous driving, natural language processing, and scientific study.
神经网络的光学实现（ONNs）通过利用光学的大带宽和高并行性等技术优势，预示着下一代高速且节能的深度学习计算。然而，由于数值域不完整、硬件规模有限或数值精度不足等问题，现有的大多数ONNs仅针对基本分类任务进行研究。鉴于回归是深度学习的一种基本形式，且在当前人工智能应用中占很大一部分，掌握深度学习回归对于ONNs的进一步发展和部署至关重要。在此，我们展示了一种能够完成深度学习回归任务的硅基光学相干点积芯片（OCDC）。该OCDC采用光场在完整的实值域而非仅在正域中进行运算。通过复用，单个芯片可在任何复杂度的神经网络中进行矩阵乘法和卷积运算。此外，鉴于芯片架构的简单性，可通过原位反向传播控制来补偿硬件偏差。因此，OCDC满足了复杂回归任务的要求，并且我们成功展示了一个具有代表性的神经网络——AUTOMAP（一种用于图像重建的前沿神经网络模型）。OCDC和一台32位数字计算机重建图像的质量相当。据我们所知，在ONN芯片上执行此类先进回归任务尚无先例。预计OCDC能够推动ONNs在现代人工智能应用（包括自动驾驶、自然语言处理和科学研究）中取得新成就。
