CCF: SHF: Small: Collaborative Research: Domain-specific Reconfigurable Processor for Time-Series Data Mining and Monitoring

该提案将研究提高执行实时医疗监控的可穿戴设备(例如物联网)的性能并降低成本和能源消耗的技术。其目的是演示如何构建可编程集成电路，在提供监控功能的同时保持足够小，以便佩戴者方便和不引人注目。作为一个鼓舞人心的例子，这种设备可以检测和预测根本重要的医疗问题，如心包填塞，这是一种危及生命的紧急情况，心包(心脏周围的囊)充满液体，阻止心脏泵血，迅速直接导致死亡。这一努力的更广泛影响包括：降低医疗监测和拯救生命的成本；通过新生发现研讨会向两所大学的本科生介绍可穿戴计算的硬件/软件联合设计；将女性和代表性较低的少数族裔纳入该项目；公开发布在该项目过程中开发的硬件和软件；针对数据库/数据挖掘和设计自动化研究社区的教程传播。技术方法将涉及创建可添加到嵌入式处理器和微控制器中的专用集成电路硬件，以提高实时医疗监测应用程序的性能。这项研究的基础是观察到现实世界的数据集往往在数据的维度(数据采样率)和基数(不同值的数量)方面表现出显著的差异；例如，超声心动图(ECG)的采样频率通常为1,024赫兹和比特，尽管降维到128赫兹和基数到8比特对于实时监测来说已经足够。根本的挑战是，最小维度和基数可能因任务而异，因个体而异，甚至可能因特定个体的时数而异。将最小描述长度(MDL)原理作为潜在的解决方案来寻找数据源的内在维度和基数，从而通过降噪来减少数据量和提高检测精度。然后，这些信息将被用来设计特定于领域的自适应体系结构，该体系结构可以利用减少的数据量来提高吞吐量和延长电池寿命。

This proposal will investigate techniques to improve performance and reduce the cost and energy consumption of wearable devices (e.g., Internet-of-Things) that perform real-time medical monitoring. The objective is to demonstrate how to construct programmable integrated circuits that can provide monitoring capabilities while remaining small enough to be convenient and unobtrusive to the wearer. As a motivating example, such a device could detect and predict medical problems of fundamental importance such as pericardial tamponade, a life-threatening emergency where in the pericardium (a sac surrounding the heart) fills with fluid and prevents the heart from pumping blood, leading quickly and directly to death. Broader impacts of this effort include: reducing the cost of medical monitoring and saving lives; introduction of undergraduate students at both institutions to hardware/software co-design for wearable computing through a Freshman Discovery Seminar; inclusion of women and underrepresented minorities in the project; public release of hardware and software developed in the course of this project; and tutorial dissemination targeting the database/data mining and design automation research communities.The technical approach will involve the creation of application-specific integrated circuit hardware that can be added to embedded processors and microcontrollers to improve the performance of real-time medical monitoring applications. The research is based on the observation that real world data sets often exhibit a significant disparity in the dimensionality (data sampling rate) and cardinality (number of distinct values) of the data; for example, echocardiograms (ECGs) are often sampled at 1,024 Hz and 64-bits, although reducing the dimensionality to 128 Hz and the cardinality to 8-bits suffice for real-time monitoring. The fundamental challenge is that the minimum dimensionality and cardinality may vary from task-to-task, from individual-to-individual, and possibly even from hour-to-hour for a given individual. The Minimum Description Length (MDL) principle will be investigated as a potential solution to find the intrinsic dimensionality and cardinality of the data source, which can reduce data volume and improve detection accuracy by noise reduction. This information will then be leveraged to design domain-specific adaptive architectures that can exploit this reduced data volume to improve throughput and enhance battery lifetime.