Brain-inspired photonic computing for efficient next-generation telecommunications networks

用于高效下一代电信网络的受大脑启发的光子计算

• 批准号: 550313-2020
• 负责人: Morandotti, RobertoR
• 金额: $ 26.49万
• 依托单位: Institut national de la recherche scientifique
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=752638
• 关键词: Brain; inspired; photonic; computing; efficient

Upcoming socio-economical advances in the context of the Internet of Things, big data networks, and smart applications are placing stress on current information and communication technology infrastructure. Such demands are reaching the limit of conventional technological solutions, implying a need for novel concepts in order to process data at scale. Recurrent neural networks (RNNs) are a brain-inspired machine learning paradigm that is especially suited for parallel computing and pattern recognition, offering a processing acceleration of several orders of magnitudes. Photonic RNNs promise a versatile platform for speed of light data processing comparable in accuracy with classical software approaches, at a reduced footprint and power consumption. However, current implementations are often lacking in terms of computational power, system simplicity, or processing speed, due to the need for electro-optical conversion. In the proposed project, we will, together with our industrial partner Huawei Canada, focus on three main objectives to overcome such limitations: (i) the first demonstration of a functional photonic neuromorphic platform exploiting multiple degrees of freedom for information processing at unprecedented speeds, (ii) its real-world application towards telecommunications data processing, and (iii) the development of an efficient and compact prototype. We aim to utilize on-chip nonlinear components to target the tasks of nonlinear channel equalization and signal regeneration. The envisioned prototype will, for the first time, reveal the full potential of photonic RNNs suitable for large-scale production. The project outcome will benefit the Canadian market through the training of highly-qualified personnel (HQP) in the uniquely combined fields of integrated photonics and machine learning, with the potential for the HQP to become future leaders in both industry and academia. The commercialization of our technology will strengthen the role of Canada in the high-tech sectors of human-machine interaction and telecommunications, thus paving the way to cope with the demands of upcoming high-density transmission standards such as 6G and 400 Gb/s systems.

物联网、大数据网络和智能应用背景下即将到来的社会经济进步给当前的信息和通信技术基础设施带来了压力。这些需求已经达到了传统技术解决方案的极限，这意味着需要新的概念来大规模处理数据。循环神经网络 (RNN) 是一种受大脑启发的机器学习范例，特别适合并行计算和模式识别，可提供几个数量级的处理加速。光子 RNN 有望提供一个多功能平台，可实现光速数据处理，其精度可与经典软件方法相媲美，同时减少占用空间和功耗。然而，由于需要电光转换，当前的实现通常在计算能力、系统简单性或处理速度方面缺乏。在拟议的项目中，我们将与我们的工业合作伙伴华为加拿大公司一起，重点关注三个主要目标，以克服这些限制：（i）首次演示功能性光子神经形态平台，利用多个自由度以前所未有的速度进行信息处理， (ii) 其在电信数据处理方面的实际应用，以及 (iii) 开发高效且紧凑的原型。我们的目标是利用片上非线性组件来完成非线性通道均衡和信号再生的任务。设想的原型将首次揭示适合大规模生产的光子 RNN 的全部潜力。该项目的成果将通过在集成光子学和机器学习的独特组合领域培训高素质人才（HQP），使加拿大市场受益，使HQP有潜力成为行业和学术界的未来领导者。我们技术的商业化将加强加拿大在人机交互和电信高科技领域的作用，从而为应对即将到来的高密度传输标准（例如6G和400 Gb/s系统）的需求铺平道路。