NCS-FO: Integrated neuroengineering of brain-inspired algorithms for parsing realistic environments

NCS-FO：用于解析现实环境的受大脑启发的算法的集成神经工程

• 批准号: 2123862
• 负责人: Thomas Cleland
• 金额: $ 100万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-15 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2123862&HistoricalAwards=false
• 关键词: NCS; FO; Integrated; neuroengineering; brain

For some tasks, modern computers vastly outperform the human brain – for example, large-scale numerical calculations, or the precisely accurate recall of organized information. But for other important tasks, the brains of humans and other animals are far superior to any computing system that has been built, both in terms of what they can do and in terms of the startlingly low energy required. For example, people can recognize familiar individuals at a distance, not only by their facial features, but by gait or other subtleties of movement that they might not even be able to articulate. People and other animals rapidly acquire information from their environments, but also are able to intelligently apply that information under novel, unforeseen circumstances. The study of brain-inspired computing is devoted to learning fundamental new ways to think about how computers work with information, so that they can perform better on such weakly-defined, open-world problems. In parallel, advances in physics have produced new optical materials and methods that can perform computations very rapidly and with extraordinarily low energy costs – if the problems of interest can be structured in ways compatible with these brain-inspired computing techniques. This project seeks to develop a brain-inspired computing network that learns rapidly and solves a set of real-world identification tasks, and to deploy this network onto portable devices as well as custom testing platforms built with these advanced physical substrates. A key goal is to show how these brain-inspired computing methods can achieve superior performance on open-world problems, most radically so when deployed on next-generation optical computer platforms. In contrast to contemporary deep networks, the brain-inspired networks described in this proposal are based on heterogeneous elements and feedback-mediated dynamical systems, and operate based on fully localized computations that obviate the need for shared memory resources. Consequently, they learn rapidly, and when deployed on neuromorphic platforms such as Intel Loihi they exhibit increased speed and tremendously reduced energy budgets. Importantly, state of the art photonic computing substrates are directly compatible with neuromorphic computational architectures, suggesting that they will be compelling platforms for these decentralized, brain-inspired computing algorithms. The intellectual merit of this project is to develop, deploy, and benchmark an established set of decentralized, brain-inspired algorithms designed for successful sensory identification under unpredictable, open-world conditions on a range of platforms, including leading-edge photonic computational substrates. Specifically, the algorithms will be extended to incorporate higher-order brain-inspired circuit properties, deployed onto portable device platforms for use in the field, and also deployed and tested on photonic substrates to demonstrate the transformational potential of these computational platforms. Broader impacts include a continuing commitment by both PIs to supervising undergraduate research experiences for students from groups underrepresented in STEM on projects directly connected with the research proposed here, as well as the potential for development of a new generation of smart devices using neuromorphic methods. PI Cleland also intends to incorporate the concepts discussed in this application into a unit of his advanced undergraduate Neural Representations seminar course.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.

在某些任务上，现代计算机的性能远远超过人脑--例如，大规模的数值计算，或对有组织信息的准确记忆。但在其他重要任务上，人类和其他动物的大脑远远优于任何已建成的计算系统，无论是就它们所能做的事情而言，还是在所需的惊人的低能量方面。例如，人们不仅可以通过面部特征，还可以通过步态或其他他们可能甚至无法表达的微妙动作来识别远处熟悉的人。人类和其他动物迅速从环境中获取信息，但也能够在新奇的、不可预见的情况下智能地应用这些信息。对大脑启发计算的研究致力于学习基本的新方法来思考计算机如何处理信息，以便它们能够更好地处理这种定义模糊的开放世界问题。与此同时，物理学的进步已经产生了新的光学材料和方法，可以非常快速地执行计算，并且能源成本非常低--如果感兴趣的问题能够以与这些大脑启发的计算技术兼容的方式构造的话。该项目寻求开发一种受大脑启发的计算网络，该网络能够快速学习并解决一系列现实世界的识别任务，并将该网络部署到便携式设备以及使用这些先进物理基板构建的定制测试平台上。一个关键的目标是展示这些大脑启发的计算方法如何在开放世界的问题上取得优异的性能，最根本的是当部署在下一代光学计算机平台上时。与当代的深层网络不同，该方案中描述的大脑启发网络基于异质元素和反馈中介的动态系统，并且基于完全局部化的计算来操作，从而消除了对共享内存资源的需求。因此，它们的学习速度很快，当部署在Intel Loihi等神经形态平台上时，它们表现出更快的速度和极大的能源预算。重要的是，最先进的光子计算基板与神经形态计算体系结构直接兼容，这表明它们将成为这些分散的、受大脑启发的计算算法的引人注目的平台。该项目的智力优势是开发、部署和基准一套已建立的分散的、受大脑启发的算法，设计用于在一系列平台上(包括尖端的光子计算基板)在不可预测的开放世界条件下成功进行感觉识别。具体地说，这些算法将被扩展到包含更高阶脑启发的电路特性，部署到便携式设备平台上用于现场，还将部署和测试在光子衬底上，以展示这些计算平台的转型潜力。更广泛的影响包括两家PI继续致力于监督STEM中代表性较低群体的学生在与这里提出的研究直接相关的项目中的本科研究经验，以及使用神经形态方法开发新一代智能设备的潜力。皮克莱兰还打算将本申请中讨论的概念纳入他的高级本科生神经表现研讨会课程的一个单元。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。