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

CCF：SHF：小型：协作研究：用于时间序列数据挖掘和监控的特定领域可重构处理器

• 批准号: 1527127
• 负责人: Abdullah Mueen
• 金额: $ 24.46万
• 依托单位: University of New Mexico
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1527127&HistoricalAwards=false
• 关键词: CCF; SHF; Small; Collaborative; Research

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.

本提案将研究提高可穿戴设备性能并降低其成本和能耗的技术（例如，物联网（Internet of Things），可执行实时医疗监控。其目的是演示如何构建可编程集成电路，可以提供监测功能，同时保持足够小，方便和不显眼的佩戴者。作为一个激励性的例子，这样的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，其中心包（心脏周围的囊）充满液体并阻止心脏泵送血液，从而迅速直接导致死亡。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;通过新生探索研讨会，向两个机构的本科生介绍可穿戴计算的硬件/软件共同设计;将妇女和代表性不足的少数群体纳入项目;公开发布在该项目过程中开发的硬件和软件;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，以提高真实的性能。医疗监控应用。该研究基于以下观察结果：真实的世界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，尽管将维度降至128 Hz并将基数降至8位足以进行实时监测。最根本的挑战是，最小维度和基数可能会因任务而异，因个体而异，甚至可能因给定个体的小时而异。最小描述长度（MDL）原则将被研究作为一个潜在的解决方案，以找到内在的维度和基数的数据源，这可以减少数据量，提高检测精度的降噪。然后，这些信息将被用来设计特定领域的自适应架构，这些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。