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EAGER: Characterizing and Accelerating Real-Time IoT Applications using FPGAs

EAGER：使用 FPGA 表征和加速实时物联网应用

基本信息

批准号：
1742899
负责人：
Matthew Tolentino
金额：
$ 17.49万
依托单位：
University of Washington
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-08-15 至 2019-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1742899&HistoricalAwards=false
关键词：
EAGER Characterizing Accelerating Real Time

项目摘要

Rapid advances in technology scaling have driven the development of inexpensive sensing platforms with limited compute capabilities. Deployed at the network edge, these platforms enable the instrumentation of the physical world. Using offline analytical pipelines on cloud resources, municipalities have leveraged this data to manage and even improve environmental conditions promoting public health, safety, and quality of life. However, for emergency scenarios, such as large-scale building fires where network access is limited, relying on off-site cloud resources for real-time analysis of sensor data is impractical. This project investigates how hardware acceleration can be efficiently integrated into computing platforms at the network edge, enabling the use of real-time analytical pipelines at the edge to create smart, intelligent systems without requiring access to cloud resources.This project addresses the problem of efficiently integrating acceleration within edge devices by characterizing and analyzing the power and performance of real-time workloads with a focus on high-impact applications used by first responders. Based on this characterization, we are building analytical performance and power models for these emergent workloads. These models provide insight on prospective hardware acceleration techniques and identify the limits of acceleration given the power constraints for our specific real-time use cases. Using these models we are integrating Field-Programmable Gate Arrays (FPGAs) into prototype edge devices, developing novel FPGA images for key analytical pipelines within our first responder workloads, and evaluating the power and performance impact within real systems. This work provides unique insight into the composition of future edge devices to enable real-time analytics.

技术规模的快速发展推动了计算能力有限的廉价传感平台的发展。这些平台部署在网络边缘，使物理世界的仪器仪表成为可能。市政当局利用云资源上的离线分析管道，利用这些数据来管理甚至改善环境条件，促进公共健康、安全和生活质量。然而，对于紧急情况，如网络接入有限的大规模建筑火灾，依靠非现场云资源对传感器数据进行实时分析是不切实际的。该项目研究如何将硬件加速有效地集成到网络边缘的计算平台中，从而在不需要访问云资源的情况下，在边缘使用实时分析管道来创建智能、智能的系统。该项目通过描述和分析实时工作负载的功率和性能，重点关注第一响应者使用的高影响应用程序，解决了在边缘设备中有效集成加速的问题。基于此特征，我们正在为这些紧急工作负载构建分析性能和功率模型。这些模型提供了对未来硬件加速技术的见解，并在我们特定的实时用例的功率约束下确定了加速的限制。使用这些模型，我们将现场可编程门阵列（FPGA）集成到原型边缘设备中，为第一响应者工作负载中的关键分析管道开发新型FPGA图像，并评估实际系统中的功率和性能影响。这项工作提供了对未来边缘设备组成的独特见解，以实现实时分析。