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: 1528181
• 负责人: Philip Brisk
• 金额: $ 26.22万
• 依托单位: University of California-Riverside
• 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 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1528181&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.

该提案将调查改善性能并降低执行实时医疗监控的可穿戴设备（例如，thingings）的成本和能源消耗的技术。目的是演示如何构建可编程的集成电路，这些电路可以提供监视功能，同时保持足够小，以方便且佩戴者不引人注目。作为一个激励的例子，这种装置可以检测和预测基本重要性的医学问题，例如心包卫生膜，这是一种危及生命的紧急情况，在心包中（围绕心脏的囊）充满了液体，并防止心脏泵入血液，迅速直接导致死亡。这项工作的更广泛的影响包括：降低医疗监测和挽救生命的成本；通过大一新生发现研讨会引入了两个机构的本科生，以用于可穿戴计算的硬件/软件共同设计；在该项目中包括妇女和代表性不足的少数民族；在该项目过程中开发的硬件和软件的公开发布；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特定于应用程序的集成电路硬件，这些硬件可以添加到嵌入式处理器和微控制器中，以提高实时医疗监控应用的性能。该研究基于这样的观察，即现实世界数据集通常在数据（数据采样率）和数据的基数（数据采样率）（数量不同的值）上表现出显着差异。例如，超声心动图（ECG）通常以1,024 Hz和64位采样，尽管将尺寸降低至128 Hz，而基数则足以实时监测。根本的挑战是，最小维度和心脏性可能从任务到任务，从个人到个体，甚至可能从每小时到每小时，对于给定的人来说。最小描述长度（MDL）原理将被研究为找到数据源的固有维度和基数的潜在解决方案，这可以减少数据量并通过降低噪声来提高检测准确性。然后，将利用此信息来设计特定于域的自适应体系结构，这些体系结构可以利用此减少的数据量来改善吞吐量并增强电池寿命。

项目成果

FA-LAMP: FPGA-Accelerated Learned Approximate Matrix Profile for Time Series Similarity Prediction

FA-LAMP：用于时间序列相似性预测的 FPGA 加速学习近似矩阵配置文件

• DOI: 10.1109/fccm51124.2021.00013
• 发表时间: 2021
• 期刊: International Symposium on Field-Programmable Custom Computing Machines
• 作者: Kalantar, Amin;Zimmerman, Zachary;Brisk, Philip
• 通讯作者: Brisk, Philip

An FPGA-based Programmable Vector Engine for Fast Fully Homomorphic Encryption over the Torus

基于 FPGA 的可编程矢量引擎，用于环面上的快速全同态加密

• 发表时间: 2021
• 期刊: SPSL: Secure and Private Systems for Machine Learning (ISCA Workshop
• 作者: Gener, Serhan;Newton, Parker;Tan, Daniel;Richelson, Silas;Lemieux, Guy;Brisk, Philip
• 通讯作者: Brisk, Philip

Matrix Profile Index Prediction for Streaming Time Series

流时间序列的矩阵轮廓指数预测

• 发表时间: 2020
• 期刊: Workshop on ML for Systems at NeurIPS 2020
• 作者: Shahcheraghi, Maryam;Cappon, Trevor;Oymak, Samet;Papalexakis, Evangelos;Keogh, Eamonn;Zimmerman, Zachary;Brisk, Philip
• 通讯作者: Brisk, Philip

Matrix Profile Index Approximation for Streaming Time Series

• DOI: 10.1109/bigdata52589.2021.9671484
• 发表时间: 2021-12
• 期刊: 2021 IEEE International Conference on Big Data (Big Data)
• 作者: Maryam Shahcheraghi;Trevor Cappon;Samet Oymak;E. Papalexakis;Eamonn J. Keogh;Zachary Zimmerman;P. Brisk