用于深度神经网络加速的光计算：基础、最新进展与新兴方向
Optical Computing for Deep Neural Network Acceleration: Foundations, Recent Developments, and Emerging Directions
AI总结：
本文综述了光计算加速深度神经网络的基础、最新进展与未来方向，探讨了光器件工程、架构设计及软硬件协同设计技术，以提升多平台DNN工作负载的性能与能效。
摘要：
计算机视觉、自然语言处理、图处理和序列预测等领域的新兴人工智能应用越来越依赖深度神经网络（DNN）。这些DNN在训练和推理时需要大量的计算和内存资源。CPU、GPU和TPU等传统计算平台正难以跟上日益复杂和多样的DNN的需求。光计算代表了一种令人兴奋的新范式，能够以光速加速DNN工作负载。在本文中，我们讨论了光计算的基础和最新进展，重点在于DNN加速。我们描述了多种有前景的方法，用于工程化光器件、增强光电路以及设计架构，使光计算能够适应各种DNN工作负载。此外，还讨论了硬件/软件协同设计的新技术，这些技术能智能地调整和映射DNN模型，以提升在光计算平台上的性能和能效，涵盖高性能以及资源受限的嵌入式、边缘和物联网平台。最后，突出了该领域研究的几个开放性问题和未来方向。
英文摘要：
Emerging artificial intelligence applications across the domains of computer vision, natural language processing, graph processing, and sequence prediction increasingly rely on deep neural networks (DNNs). These DNNs require significant compute and memory resources for training and inference. Traditional computing platforms such as CPUs, GPUs, and TPUs are struggling to keep up with the demands of the increasingly complex and diverse DNNs. Optical computing represents an exciting new paradigm for light-speed acceleration of DNN workloads. In this article, we discuss the fundamentals and state-of-the-art developments in optical computing, with an emphasis on DNN acceleration. Various promising approaches are described for engineering optical devices, enhancing optical circuits, and designing architectures that can adapt optical computing to a variety of DNN workloads. Novel techniques for hardware/software co-design that can intelligently tune and map DNN models to improve performance and energy-efficiency on optical computing platforms across high performance and resource constrained embedded, edge, and IoT platforms are also discussed. Lastly, several open problems and future directions for research in this domain are highlighted.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
