CPS: Small: Brain-Inspired Memorization and Attention for Intelligent Sensing

网络物理应用通常使用机器学习算法分析收集的传感器数据。许多现有的传感系统缺乏对目标的智能，并且天真地生成大规模数据，使得通信和计算成本非常高。然而，在许多情况下，传感器生成的数据只包含传感器活动的一小部分有用信息。例如，机器学习算法持续处理用于环境/安全监测的视觉传感器，以检测敏感活动。尽管如此，这些传感器只能在短时间内提供有用的信息。另一方面，生物传感器智能地产生的数据量要少得多。该项目开发机器学习算法，为传感器提供实时反馈，以确保它们只生成用于学习目的所需的数据。该方法有望将传感器的数据减少4个数量级。这项研究的结果将广泛影响物联网应用中使用的许多传感器，包括基础设施、移动设备、自主系统、机器人和医疗保健。该项目还将通过协同推广计划和教育活动，包括K-12学生项目、本科生研究机会和新课程开发，支持代表性不足的少数民族学生。本项目介绍的研究方法旨在对传感系统进行根本性的改变，以使未来的传感器智能化，适用于广泛的网络物理应用。首先，该项目将开发新颖的大脑启发学习算法，该算法可以为传感模块提供快速实时的反馈，以智能地控制传感器的数据生成速率。这种反馈还使传感器意识到目标任务，从而实现态势感知。其次，该项目将开发一个新颖的框架，该框架与传感电路和大脑启发算法紧密集成，以闭环方式动态控制传感器功能。所提出的硬件平台利用学习算法的鲁棒性来设计高度近似、并行和高效的近传感器计算平台。最后，本项目旨在评估该框架在多个大规模系统上的有效性。原型将在一个已建立的开源库下全面发布，供公众传播。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Cyber-physical applications often analyze collected sensor data using machine learning algorithms. Many existing sensing systems lack intelligence about the target and naively generate large-scale data, making communication and computation significantly costly. In many cases, however, the data generated by sensors only contain useful information for a small portion of the sensor activity. For example, machine learning algorithms continuously process the visual sensors used for environmental/security monitoring to detect sensitive activities. Still, these sensors only carry out useful information for a short time. On the other hand, biological sensors intelligently generate orders of magnitude less amount of data. This project develops machine learning algorithms that provide real-time feedback to sensors to ensure they only generate data needed for learning purposes. The approach is expected to provide up to four orders of magnitude data reduction from sensors. The results from this research will broadly impact many sensors used in internet-of-things applications, including infrastructure, mobile devices, autonomous systems, robotics, and healthcare. The project will also support underrepresented minority students through synergistic outreach plans and educational activities, including programs for K-12 students, undergraduate research opportunities, and new course development.The research approaches introduced in this project aim to make fundamental changes to sensing systems in order to make future sensors intelligent for a wide range of cyber-physical applications. First, this project will develop novel brain-inspired learning algorithms that can provide fast and real-time feedback to the sensing module to intelligently control the rate of data generation from sensors. This feedback also makes sensors aware of the target task, enabling situational awareness. Second, the project will develop a novel framework that tightly integrates with a sensing circuit and brain-inspired algorithms to dynamically control the sensor functionality in a close-loop manner. The proposed hardware platform exploits the robustness of learning algorithms to design near-sensor computing platforms that are highly approximate, parallel, and efficient. Finally, this project aims to evaluate the effectiveness of the framework on multiple large-scale systems. The prototype will be fully released under an established open-source library for public dissemination.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.