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

基本信息

  • 批准号:
    2211989
  • 负责人:
  • 金额:
    $ 100万
  • 依托单位:
  • 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

项目摘要

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。

项目成果

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Andrea Fumagalli其他文献

An end-to-end DPDK-integrated open-source 5G standalone Radio Access Network: A proof of concept
  • DOI:
    10.1016/j.comnet.2024.110533
  • 发表时间:
    2024-08-01
  • 期刊:
  • 影响因子:
  • 作者:
    Abhishek Bhattacharyya;Shunmugapriya Ramanathan;Andrea Fumagalli;Koteswararao Kondepu
  • 通讯作者:
    Koteswararao Kondepu
Edge/Cloud Co-operative Autonomous Driving Vehicular (ADV) control technologies
边缘/云协同自动驾驶车辆(ADV)控制技术
  • DOI:
  • 发表时间:
    2018
  • 期刊:
  • 影响因子:
    0
  • 作者:
    山中直明;岡本聡;鑓田幸大;村中孝行;Andrea Fumagalli
  • 通讯作者:
    Andrea Fumagalli
Reliable edge-to-core optical networks: An optimal algorithm for maximal path diversity
  • DOI:
    10.1016/j.comnet.2024.110268
  • 发表时间:
    2024-04-01
  • 期刊:
  • 影响因子:
  • 作者:
    Ali Shakeri;Tianliang Zhang;Shunmugapriya Ramanathan;Miguel Razo;Marco Tacca;Andrea Fumagalli
  • 通讯作者:
    Andrea Fumagalli
DDD: Distributed Dataset DNS
DDD:分布式数据集 DNS
  • DOI:
    10.1007/s10586-021-03515-w
  • 发表时间:
    2022
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Joseph White;Andrea Fumagalli
  • 通讯作者:
    Andrea Fumagalli
Slot Routing as a Solution for Optically Transparent Scalable WDM Wide Area Networks*
时隙路由作为光透明可扩展 WDM 广域网的解决方案*
  • DOI:
    10.1023/a:1010060529536
  • 发表时间:
    1999-01-01
  • 期刊:
  • 影响因子:
    1.700
  • 作者:
    Imrich Chlamtac;Andrea Fumagalli;Gosse Wedzinga
  • 通讯作者:
    Gosse Wedzinga

Andrea Fumagalli的其他文献

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{{ truncateString('Andrea Fumagalli', 18)}}的其他基金

Collaborative Research: CNS Core: MEDIUM: RUI: Optics Without Borders
合作研究:CNS 核心:MEDIUM:RUI:光学无国界
  • 批准号:
    1956357
  • 财政年份:
    2020
  • 资助金额:
    $ 100万
  • 项目类别:
    Continuing Grant
US Ignite: Collaborative Research: Track 1: Industrial Cloud Robotics across Software Defined Networks
US Ignite:协作研究:轨道 1:跨软件定义网络的工业云机器人
  • 批准号:
    1531039
  • 财政年份:
    2015
  • 资助金额:
    $ 100万
  • 项目类别:
    Standard Grant
CC*DNI Integration: PROnet: A PRogrammable Optical Network Prototype Serving the Campus
CC*DNI 集成:PROnet:服务于园区的可编程光网络原型
  • 批准号:
    1541461
  • 财政年份:
    2015
  • 资助金额:
    $ 100万
  • 项目类别:
    Standard Grant
NeTS: Medium: Collaborative Research:Digital SubCarrier Multiplexing (DSCM) Networks: from the Core to the Access
NeTS:媒介:协作研究:数字子载波复用 (DSCM) 网络:从核心到接入
  • 批准号:
    1409849
  • 财政年份:
    2014
  • 资助金额:
    $ 100万
  • 项目类别:
    Continuing Grant
NeTS: JUNO: Collaborative Research: ACTION: Applications Coordinating with Transport, IP, and Optical Networks
NetS:JUNO:协作研究:ACTION:与传输、IP 和光网络协调的应用
  • 批准号:
    1405405
  • 财政年份:
    2014
  • 资助金额:
    $ 100万
  • 项目类别:
    Standard Grant
NeTS: Large: Collaborative Research: HyperFlow - A Hybrid IP/Optical Flow Network Architecture
NeTS:大型:协作研究:HyperFlow - 混合 IP/光流网络架构
  • 批准号:
    1111329
  • 财政年份:
    2011
  • 资助金额:
    $ 100万
  • 项目类别:
    Continuing Grant
NeTS-NR: Collaborative Research: High-Speed Self-Configuring Networks Based on Cost-Effective Plug-and-Play Optical (PPO) Nodes
NeTS-NR:协作研究:基于经济高效的即插即用光(PPO)节点的高速自配置网络
  • 批准号:
    0435393
  • 财政年份:
    2004
  • 资助金额:
    $ 100万
  • 项目类别:
    Continuing Grant
Differentiated Reliability (DiR) in Multi-layer Optical Networks
多层光网络中的差异化可靠性 (DiR)
  • 批准号:
    0082085
  • 财政年份:
    2001
  • 资助金额:
    $ 100万
  • 项目类别:
    Standard Grant

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