Programmable Nanophotonics for Deep Learning and Neuromorphic Computing

用于深度学习和神经形态计算的可编程纳米光子学

• 批准号: RGPIN-2018-05249
• 负责人: Shastri, Bhavin
• 金额: $ 2.4万
• 依托单位: Queen's University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=713374
• 关键词: Programmable; Nanophotonics; Deep; Learning; Neuromorphic

SYNOPSIS. The birth of computers shaped 20th century society and science. After decades of exponential improvement, the performance of von Neumann architectures in speed, efficiency, and generality, has begun to run into fundamental limits, as the shrinking of transistors reaches its physical limits. As a result, the gap between current computing capabilities and computing needs is widening. This insufficiency is apparent in problems involving complex systems, big data, or real-time requirements. Forays into unconventional (non-von Neumann) computing have only been partially successful due to the limitations in bandwidth and energy consumption posed by metal interconnects. VISION orm state-of-the-art microelectronic processors in energy efficiency and computational speeds by seven- and four orders-of-magnitude, respectively. Scientific objectives include: 1) devices thrustenergy efficient (attoJoule/operation) photonic neurons with graphene-based electro-optic modulators; 2) architectures thrustscalable and programmable silicon photonic neural network architectures; and 3) applications thrustphotonic processors for generalized neuromorphic computing tasks including deep learning and nonlinear optimization for model predictive control. This program focuses on these computing tasks as they are notoriously difficult to solve. IMPACT. An experimentally-driven investigation of neuromorphic nanophotonics will serve as the first feasibility proof of using integrated photonics for scalable information processing. The proposed program has the potential to shape the emerging field of generalized compute engines beyond von-Neumann architectures and help redefine their physical limitations. The resulting technology has the potential to transform social, scientific, and technological sectors including self-navigating vehicles, bio-informatics, security, and big data. This research will contribute to the culture of innovation excellence across the scientific community and Canada. The multi-disciplinary nature of the program promises to foster collaborative ties across Canadian academic institutions and the private sector. The program's educational impact rests on uniquely positioning students for a readied workforce in academia or industry to drive tomorrow's advancements in photonics, applied physics, and engineering for 21st century challenges.

概要。计算机的诞生塑造了20世纪的社会和科学。经过数十年的指数改进，von Neumann架构在速度，效率和通用性方面的表现已开始遇到基本限制，因为晶体管的缩小达到了其物理极限。结果，当前的计算功能与计算需求之间的差距正在扩大。在涉及复杂系统，大数据或实时要求的问题中，这种不足是显而易见的。由于金属互连造成的带宽和能耗的局限性，因此对非常规（非冯·诺伊曼）计算的尝试仅部分成功。 视觉ORM在能源效率和计算速度方面的最先进的微电处理器分别达到七个和四个级别。科学目标包括：1）带有基于石墨烯的电光调节剂的设备推动力（Attojoule/Operation）光子神经元； 2）架构推动且可编程的硅光子神经网络体系结构； 3）应用程序处理器，用于通用神经形态计算任务，包括深度学习和非线性优化，用于模型预测性控制。该程序侧重于这些计算任务，因为众所周知，它们很难解决。 影响。对神经形态纳米光子学的实验驱动的研究将是使用集成光子学进行可扩展信息处理的第一个可行性证明。该计划的计划有可能塑造冯·尼曼（Von-Neumann）体系结构以外的广义计算引擎的新兴领域，并有助于重新定义其身体局限性。最终的技术有可能改变社会，科学和技术部门，包括自动化的车辆，生物信息学，安全性和大数据。这项研究将有助于整个科学界和加拿大的创新卓越文化。该计划的多学科性质有望建立跨加拿大学术机构和私营部门的合作关系。该计划的教育影响取决于为学术界或行业的良好劳动力提供独特的定位学生，以推动明天在21世纪挑战方面的光子学，应用物理学和工程学方面的进步。