Collaborative Research: CNS Core: Medium: TeTON: A Testbed and a Toolkit for Expediting Investigation of and Accelerating Advancements in All-Optical Neural Networks

在大脑中，数十亿个神经元不断地从彼此收集电信号，通过这些信号的自然非线性组合，人类能够处理信息（视觉，听觉，触觉等），学习如何识别模式，并做出快速决策。这些相互连接的神经元形成了通常所说的神经网络。该项目旨在开发一个全光学（人工）神经网络（AONN）测试平台和相关软件工具包，以加快和加速光学人工智能的进步，包括具有自动机器学习能力的可编程多层AONN硬件和用于公共教育的开源软件包。AONN测试平台建立在自由空间傅立叶光学和超低光级非线性光学的基础上，可以有效地利用光子重建类似于人脑中执行的功能。利用光的电磁波性质，与迄今为止用于实现人工智能（AI）的更传统的软件和定制的基于电子的人工神经网络相比，所产生的AONN预计将运行得更快，消耗的能量也将显著减少。发现如何最好和最有效地实现AONN解决方案将改变实现AI的方式。能够以光速思考的人工大脑可以为实现许多新的和目前无法想象的实时应用铺平道路，例如检查大量制造对象的质量，并在需要快速生产调整的情况下向生产工厂提供几乎即时的反馈。 使用AONN能够快速识别模式及其异常，将在医学，教育，制造业，交通运输，农业，自然科学等领域中找到许多其他应用。通过摄像机观察运动员或患者在执行特定任务时进行康复所生成的每个视频帧可以在不到一毫秒的时间内由AONN单独分析。不正确或次优的身体运动将很容易被AONN检测到，从而为运动员或患者提供即时反馈，而运动员或患者又可以不断纠正他们的姿势以实现改善的结果。 该项目支持本科生和研究生的职业发展，他们在这个跨学科的项目上共同工作。课程和宣传材料由得克萨斯大学达拉斯分校和堪萨斯大学编写，并与公众分享，该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响进行评估，被认为值得支持审查标准。

In a brain, billions of neurons continuously collect electrical signals from one another and through natural non-linear combination of these signals human beings are able to process information (visual, audio, tactile, etc.), learn how to recognize patterns, and make quick decisions. These interconnected neurons form what is commonly known as a neural network. This project aims to develop an all-optical (artificial) neural network (AONN) testbed and related software toolkit for expediting and accelerating advancements in optical artificial intelligence, including a programmable multi-layer AONN hardware with automatic machine learning capability and an open-source software package for public education. The AONN testbed builds upon free-space Fourier optics and ultra-low light level nonlinear optics, which can efficiently recreate with photons functionalities similar to those that are performed in the human brain. Making use of the electromagnetic wave nature of light the resulting AONN is expected to run much faster and consume significantly less energy when compared to the more conventional software and custom electronic-based artificial neural networks that have been applied to realize artificial intelligence (AI) so far.Discovering how to best and most effectively implement AONN solutions is going to change the way in which AI is achieved. An artificial brain that can think at the speed-of-light can pave the way to the realization of a number of new and currently unthinkable real-time applications, like for example inspecting the quality of a large number of manufactured objects and providing virtually instantaneous feedback to the production plant in the event that swift production adjustments are required. Being able to quickly recognize patterns and their anomalies using AONN will find many other applications in the fields of medicine, education, manufacturing, transportation, agriculture, natural science, etc. The following example may illustrate the potential advantages of implementing and using AONN. Each video frame generated by a camera observing an athlete or a patient going though rehabilitation while performing specific tasks could be individually analyzed by AONN in less than one millisecond. Improper or suboptimal body motions would be readily detected by AONN, hence providing instantaneous feedback to the athlete or patient who in turn could continuously correct their posture to achieve improved outcomes. This project supports career development of the undergraduate and graduate students, who work together on this cross-disciplinary project. Courses and outreach materials are developed at the University of Texas at Dallas (UTD) and the University of Kansas (KU) and shared with the public, with an emphasis on engaging with students from underrepresented communities and contributing to future student diversity in STEM disciplines.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.