Artificial Intelligence (AI) Mode-Space Optical/Quantum Processor Design for Energy-Autonomous AI Applications

在过去的六年里，Liboiron-Ladouceur博士发现并展示了光学模式如何导致更高的吞吐量系统，这要归功于硅光子（SiPh）集成电路的技术进步。最近，Liboiron-Ladouceur博士通过开发基于AI的算法来开发性能更高、更紧凑的设备，从而发现了被称为逆向设计的强大设计方法。研究成果揭示了我们如何超越目前依赖人类经验和直觉的传统设计方法。与此同时，Liboiron-Ladouceur博士最近开发了一种用于计算的光学处理器，可以实现矢量和矩阵的数学变换。这一研究成果揭示了光子在低损耗光信道中处理和计算数据的固有特性，超出了它们的传输能力。光学计算可以带来更好的能源效率，并成为现代计算机架构的加速器。初步工作表明，在一个实际的框架占制造和能源使用的预测精度良好。然而，将光学处理器尺寸增加到所需的矢量尺寸仍然是一个重要的挑战。事实上，所需的处理器尺寸，例如在自主AI应用中，比通过传统设计的光子集成电路可实现的处理器尺寸大一到两个数量级。 拟议的发现计划的研究目标调查的光学模式，以增加利用最近的光子集成电路的设计方法的光处理器的大小。模分多路复用将模式视为主要用于传输目的的正交数据信道。在发现计划中，将矢量大小从数十个元素扩展到数百个元素，充分利用了Liboiron-Ladouceur博士在基于SiPh模式的器件开发方面的重大贡献。她的研究方法利用新的逆向设计技术中的机器学习算法来开发操作光子进行计算所需的设备。这些技术将允许将器件面积从几十微米减小到几微米，同时在插入损耗、串扰和对工艺变化的鲁棒性方面保持所需的性能。 所提出的研究计划的好处是优秀的重要影响，从逆向设计技术导致光子集成电路的范式转变。模式空间光学处理器的可行性将为自动驾驶汽车中的AI应用带来节能计算平台。拟议的发现计划允许多学科，包容性和多样化的HQP培训平台。学生将通过行业认可的模拟工具和实验方法获得宝贵的研究技能，同时培养他们作为未来世界领导者的创造性和批判性思维。

Over the last six years, Dr. Liboiron-Ladouceur discovered and demonstrated how optical modes lead to higher throughput systems, thanks to technology advancements in silicon photonics (SiPh) integrated circuits. Recently, Dr. Liboiron-Ladouceur uncovered powerful design approaches referred as inverse design by developing AI-based algorithms towards more performing and compact devices. The research outcomes reveal how we can surpass conventional design methodology currently relying on human experience and intuition. In parallel, Dr. Liboiron-Ladouceur has recently developed an optical processor for computation enabling mathematical transformation of vectors and matrices. This research outcome reveals the inherent properties of photons for processing and computing data beyond their transmission capabilities in low-loss optical channels. Optical computation can lead to better energy efficiency and be accelerators in modern computer architectures. The initial work demonstrates good prediction accuracy in a practical framework accounting for fabrication and energy usage. However, increasing the optical processor size to the required vector dimensions remains an important challenge. Indeed, the processor sizes needed, such as in autonomous AI applications, are greater by one to two orders of magnitude than what is achievable through conventionally designed photonic integrated circuits. The research objectives of the proposed discovery program investigate optical modes to increase the size of optical processors exploiting recent design methodology in photonic integrated circuits. Mode division multiplexing view modes as orthogonal data channels mainly for transmission purpose. In the discovery program, scaling the vector size from tens to hundreds of elements leverages Dr. Liboiron-Ladouceur's significant contributions in SiPh mode-based device development. Her research methodology exploits machine learning algorithms in new inverse design techniques to develop the required devices manipulating photons for computation. These techniques will allow to reduce the device area from tens of microns to a few microns while maintaining the required performance in terms of insertion loss, crosstalk, and robustness to process variations. The benefits of the proposed research program are excellent with important impact from inverse design techniques leading to a paradigm shift in photonic integrated circuits. The feasibility of a mode-space optical processor will lead to energy-efficient computing platforms for AI applications in autonomous vehicles. The proposed discovery program allows for a multidisciplinary, inclusive, and diverse HQP training platform. Students will gain valuable research skills through industry-approved simulation tools and experimental methodologies, while developing their creative and critical mindset as future world leaders.