Collaborative Research: CNS Core: Small: Towards Automated and QoE-driven Machine Learning Model Selection for Edge Inference

合作研究：CNS 核心：小型：面向边缘推理的自动化和 QoE 驱动的机器学习模型选择

基本信息

批准号：
2007115
负责人：
Shaolei Ren
金额：
$ 24.99万
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：
2020
资助国家：
美国
起止时间：
2020-10-01 至 2024-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2007115&HistoricalAwards=false
关键词：
Collaborative Research CNS Core Small

项目摘要

Edge devices, such as mobile phones, drones and robots, have been emerging as an increasingly more important platform for deep neural network (DNN) inference. For an edge device, selecting an optimal DNN model out of many possibilities is crucial for maximizing the user’s quality of experience (QoE), but this is significantly challenged by the high degree of heterogeneity in edge devices and constant-changing usage scenarios. The current practice commonly selects a single DNN model for many or all edge devices, which can only provide a satisfactory QoE for a small fraction of users at best. Alternatively, device-specific DNN model optimization is time-consuming and not scalable to a large diversity of edge devices. Moreover, the existing approaches focus on optimizing a certain objective metric for edge inference, which may not translate into improvement of the actual QoE for users. By leveraging the predictive power of machine learning and keeping users in a loop, this project proposes an automated and scalable device-level DNN model selection engine for QoE-optimal edge inference. Specifically, this project includes two thrusts: first, it exploits online learning to predict QoE for each edge device, automating deployment-stage DNN model selection; and second, it builds a runtime QoE predictor and automatically selects an optimal DNN model given runtime contextual information.This project represents an important departure from and an essential complement to the current practices in DNN model optimization. It can bring the benefits of DNN-enabled intelligence to many more resource-constrained edge devices with an optimal QoE. Additionally, it provides novel observations, insights and principles for edge inference, catalyzing the transformation of the design of DNN models into a new user-centric paradigm. This project also enables new opportunities to improve curriculum design and attract students, especially under-represented minorities, to engage in science, technology, engineering, and mathematics fields.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

边缘设备（例如手机，无人机和机器人）已成为深度神经网络（DNN）推断越来越重要的平台。对于边缘设备，从许多可能的情况下选择最佳DNN模型对于最大化用户的体验质量（QOE）至关重要，但这受到边缘设备中高度异质性和不断变化的使用情况方案的高度挑战。当前的实践通常为许多或所有边缘设备选择单个DNN模型，这只能为最多一小部分用户提供令人满意的Qoe。另外，设备特异性的DNN模型优化是耗时的，并且无法扩展到大量的边缘设备。此外，现有的方法着重于优化边缘推理的某个目标指标，这可能不会转化为用户实际QOE的改进。通过利用机器学习的预测能力并使用户保持在循环中，该项目提出了一个自动化和可扩展的设备级DNN模型选择引擎，用于qoe-oftimal Edge Edge推断。特别是，该项目包括两个推力：首先，它利用在线学习来预测每个边缘设备的Qoe，为每个边缘设备，自动化型DNN模型选择；其次，它构建了一个运行时QOE预测器，并自动选择了给定运行时上下文信息的最佳DNN模型。此项目代表了DNN模型优化中当前实践的重要偏差和基本完成。它可以通过最佳的QoE将支持DNN的智能的好处带入许多更多资源受限的边缘设备。此外，它为边缘推理提供了新颖的观察，见解和原理，从而催化DNN模型的设计转换为新的以用户为中心的范式。该项目还使新的机会可以改善课程设计和吸引力的学生，尤其是代表性不足的少数群体，从事科学，技术，工程和数学领域的参与。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛影响的审查标准来通过评估来通过评估来获得的支持。