Neuromorphic Photonic Operating Systems

现代计算的进步是通过不断探索和整合替代架构和替代物理学而实现的。神经形态光子学将光子器件物理学与分布式处理架构相结合。通过与电线和数字总线相反的片上波导多路复用网络连接，神经形态光子系统可以在纳秒时间尺度上运行，而神经形态电子对应物中的毫秒时间尺度。超快神经网络有望扩展而不仅仅是改进机器学习、预测和控制的应用，特别是针对计算具有延迟截止时间的实时问题。超快实时计算挑战存在，并且目前在认知无线电，高能物理实验，超导量子计算机，聚变能反应堆和其他潜在领域尚未解决。在这些应用中实现进步的技术将极大地影响我们作为一个社会的能力。硅光子制造领域最近的飞跃为大规模、低成本的光子处理器的批量生产创造了前所未有的机会。光子信息处理研究也大大加速;然而，研究管道是孤立和垂直的。每个研究实验室都必须精通从铺设波导到理解机器学习再到执行实验演示的一切。另一方面，所有形式的计算研究都被细分为硬件、操作系统（OS）和软件。操作系统提供了一个标准接口，使硬件和软件中的复杂想法能够独立开发，并在一台机器中协同工作。随着光子信息处理器的规模和复杂性的增长，迫切需要标准化的神经形态光子操作系统（NPOS）。拟议的研究将通过创建、验证、共享和应用NPOS来满足这些需求。NPOS的关键组件是内核、虚拟机、硬件特定的驱动程序和独立于硬件的编程接口。NPOS将在100%远程控制的实验光子测试平台上进行验证。远程功能意味着测试台访问可以从本地实验室网络顺利扩展，然后扩展到协作者内联网，然后最终扩展到公共互联网作为云服务。最后，该计划将利用NPOS探索新的应用领域，重点关注最先进的电子设备无法执行的高影响力实时应用。通过该计划，2名博士，3名硕士和3名本科生将接受实验集成光子学和神经网络编程的培训。培训将使他们成为加拿大世界级光子生态系统以及快速增长的机器学习和非传统计算领域的领导者。

The advance of modern computing is made possible through the continued exploration and integration of alternative architectures and alternative physics. Neuromorphic photonics combines photonic device physics with distributed processing architectures. By multiplexing network connections through on-chip waveguides as opposed electrical wiring and digital busses, neuromorphic photonic systems can operate on nanosecond timescales compared to the millisecond timescales found in neuromorphic electronic counterparts. Ultrafast neural networks promise to expand, not just improve, the applications of machine learning, prediction, and control, specifically for real-time problems in which the computation has a latency deadline. Ultrafast real-time computing challenges exist and are currently unsolved in cognitive radios, high-energy physics experiments, superconducting quantum computers, fusion energy reactors, and potentially other areas. Technologies that enable advances in any of these applications would immensely impact what we are capable of as a society. Recent leaps in silicon photonic manufacturing have created an unprecedented opportunity to produce large-scale, low-cost photonic processors in volume. Photonic information processing research has also accelerated considerably; however, research pipelines are silo-ed and vertical. Each research lab must become proficient in everything from laying out waveguides to understanding machine learning to performing experimental demonstrations. Research in all forms of computing, on the other hand, is sub-disciplined into hardware, operating system (OS), and software. The OS provides a standard interface that enables complex ideas in hardware and software to develop independently and work together within one machine. With an emerging potential for photonic information processors to grow in scale and complexity, standardized neuromorphic photonic operating systems (NPOS) are urgently needed. The proposed research will address these needs by creating, validating, sharing, and applying NPOSs. The key components of NPOS are a kernel, virtual machine, hardware-specific drivers, and hardware-independent programming interface. NPOS will be validated on an experimental photonic testbed that is 100% remote controlled. Remote capabilities mean that testbed access can expand smoothly from the local lab network, then to a collaborator intranet, then, ultimately, to the public internet as a cloud service. Finally, the program will leverage NPOS to explore new application areas, focusing on high-impact, real-time applications that cannot be performed by state-of-the-art electronics. Through the program, 2 PhD, 3 MSc, and 3 undergraduate students will be trained in experimental integrated photonics and neural network programming. Training will position them to become leaders in Canada's world class photonics ecosystem and rapidly growing sectors of machine learning and unconventional computing.