Programmable Nanophotonics for Deep Learning and Neuromorphic Computing

概要。计算机的诞生塑造了世纪的社会和科学。经过几十年的指数级改进，冯·诺依曼架构在速度、效率和通用性方面的性能已经开始遇到根本性的限制，因为晶体管的缩小达到了其物理极限。因此，当前计算能力与计算需求之间的差距正在扩大。这种不足在涉及复杂系统、大数据或实时要求的问题中很明显。非常规的尝试（非冯·诺依曼）计算只取得了部分成功，这是由于金属互连带来的带宽和能耗限制。VISION节能（attoJoule/operation）光子神经元与石墨烯基电光调制器; 2）架构thrustscalable和可编程的硅光子神经网络架构;以及3）应用thrustphotonic处理器，用于广义神经形态计算任务，包括深度学习和模型预测控制的非线性优化。这个程序专注于这些计算任务，因为它们是出了名的难以解决。神经形态纳米光子学的实验驱动的调查将作为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。该计划有可能塑造超越冯·诺依曼架构的通用计算引擎的新兴领域，并帮助重新定义其物理限制。由此产生的技术有可能改变社会，科学和技术领域，包括自导航车辆，生物信息学，安全和大数据。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促进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独特的定位学生在学术界或工业界的准备劳动力，以推动未来的光子学，应用物理学和工程的进步，为21世纪的挑战。

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 energy 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.