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测的可穿戴设备（例如物联网）性能并降低成本和能耗的技术。目的是演示如何构建可编程集成电路，该集成电路可以提供监控功能，同时保持足够小，以便对佩戴者来说方便且不显眼。作为一个激励人心的例子，这种设备可以检测和预测具有根本重要性的医疗问题，例如心包填塞，这是一种危及生命的紧急情况，心包（心脏周围的囊）充满液体并阻止心脏泵血，从而迅速直接导致死亡。这项努力的更广泛影响包括： 降低医疗监测成本并挽救生命；通过新生发现研讨会向两个机构的本科生介绍可穿戴计算的硬件/软件协同设计；将妇女和代表性不足的少数群体纳入该项目；公开发布本项目过程中开发的硬件和软件；以及针对数据库/数据挖掘和设计自动化研究社区的教程传播。该技术方法将涉及创建专用集成电路硬件，该硬件可以添加到嵌入式处理器和微控制器中，以提高实时医疗监测应用的性能。该研究基于这样的观察：现实世界的数据集通常在数据的维度（数据采样率）和基数（不同值的数量）方面表现出显着的差异；例如，超声心动图 (ECG) 通常以 1,024 Hz 和 64 位进行采样，尽管将维度降低到 128 Hz，基数降低到 8 位足以进行实时监测。根本的挑战是，最小维度和基数可能因任务不同、个体不同而不同，甚至对于特定个体而言，甚至可能因小时而异。将研究最小描述长度（MDL）原则作为寻找数据源的内在维度和基数的潜在解决方案，它可以通过降噪来减少数据量并提高检测精度。然后，将利用这些信息来设计特定领域的自适应架构，该架构可以利用减少的数据量来提高吞吐量并延长电池寿命。

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.