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

合作研究：CNS 核心：媒介：TeTON：加速全光神经网络研究和加速进步的测试平台和工具包

• 批准号: 2211989
• 负责人: Andrea Fumagalli
• 金额: $ 100万
• 依托单位: University of Texas at Dallas
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2211989&HistoricalAwards=false
• 关键词: Collaborative; Research; CNS; Core; Medium

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.

在大脑中，数十亿个神经元不断地相互收集电信号，通过这些信号的自然非线性组合，人类能够处理信息（视觉、音频、触觉等），学习如何识别模式，并做出决策。快速做出决定。这些相互连接的神经元形成了通常所说的神经网络。该项目旨在开发全光（人工）神经网络（AONN）测试台和相关软件工具包，以加快光学人工智能的进步，包括具有自动机器学习功能的可编程多层AONN硬件和开源软件公共教育软件包。 AONN 测试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，可以有效地利用类似于人脑中执行的光子功能进行重建。与用于实现人工智能 (AI) 的更传统的软件和基于定制电子的人工神经网络相比，利用光的电磁波性质，所产生的 AONN 预计运行速度更快，消耗的能量显着减少，因此探索如何最好、最有效地实施 AONN 解决方案将改变人工智能的实现方式。能够以光速思考的人造大脑可以为实现许多新的、目前无法想象的实时应用铺平道路，例如检查大量制造物体的质量并提供几乎瞬时的信息。如果需要快速调整生产，则向生产工厂反馈。 能够使用 AONN 快速识别模式及其异常，将在医学、教育、制造、交通、农业、自然科学等领域找到许多其他应用。以下示例可以说明实施和使用 AONN 的潜在优势。 AONN 可以在不到一毫秒的时间内对运动员或患者在执行特定任务时进行康复的过程进行单独分析，并由摄像机生成的每个视频帧进行单独分析。 AONN 可以轻松检测到不正确或次优的身体运动，从而向运动员或患者提供即时反馈，而运动员或患者反过来可以不断纠正其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，他们在这个跨学科项目上共同努力。课程和外展材料由德克萨斯大学达拉斯分校 (UTD) 和堪萨斯大学 (KU) 开发并与公众共享，重点是与来自代表性不足社区的学生互动，并为未来 STEM 学科的学生多样性做出贡献。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审查标准进行评估，被认为值得支持。