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.

对于某些任务，现代计算机的表现极高，例如，大规模的数值计算或精确准确地回忆有组织的信息。但是，对于其他重要任务，人类和其他动物的大脑远优于已建立的任何计算系统，无论是在他们可以做什么和所需的低能能量方面。例如，人们不仅可以通过他们的面部特征，而且可以通过聚集或其他可能无法表达的运动来认出熟悉的人。人们和其他动物迅速从其环境中获取信息，但也能够在新颖的，无法预料的情况下智能地应用该信息。对脑启发的计算的研究致力于学习基本的新方法，以考虑计算机如何与信息一起使用，以便它们可以在如此弱定义的开放世界问题上更好地执行。同时，物理学的进步产生了新的光学材料和方法，可以非常快速地执行计算的能源成本非常低 - 如果可以以与这些受脑启发的计算技术兼容的方式构造感兴趣的问题。该项目旨在开发一个以大脑启发的计算网络，该网络可以快速学习并解决一组现实世界的标识任务，并将此网络部署到便携式设备以及使用这些高级物理基质构建的自定义测试平台上。一个关键目标是展示这些受脑启发的计算方法如何在开放世界问题上实现卓越的性能，从根本上来说，当部署在下一代光学计算机平台上时。与当代深层网络相反，本提案中描述的脑启发的网络基于异构元素和反馈介导的动力系统，并基于完全局部的计算来运行，以消除共享内存资源的需求。因此，他们迅速学习，当部署在Intel Loihi等神经形态平台上时，他们暴露了速度提高，并大大降低了能源预算。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，脑启发的计算算法的引人注目的平台。该项目的智力优点是开发，部署和基准一组已建立的分散的，以脑启发的算法，旨在在不可预测的，开放世界的条件下在一系列平台上成功进行感官识别，包括领先的光电计算基板。具体而言，该算法将扩展到合并的高阶脑启发的电路属性，部署在可移植的设备平台上以供现场使用，并在光子基材上进行部署和测试，以证明这些计算平台的变换潜力。更广泛的影响包括PI的持续承诺对来自与此处提议的研究直接相关的项目的群体中的学生的监督本科研究经历，以及使用神经形态方法开发新一代智能设备的潜力。 Pi Cleland还打算将本申请中讨论的概念纳入其高级本科神经代表课程的单位。该奖项反映了NSF的法定任务，并通过使用该基金会的知识分子优点和更广泛的影响来审查标准，被认为是通过评估来支持的。