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SHF: Small: A Design Automation Methodology for Flexible Real-Time Computing based on Split and Early Exit Neural Models

SHF：小型：基于分裂和早期退出神经模型的灵活实时计算的设计自动化方法

基本信息

批准号：
2140154
负责人：
Mohammad Al Faruque
金额：
$ 50万
依托单位：
University of California-Irvine
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-02-01 至 2025-01-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2140154&HistoricalAwards=false
关键词：
SHF Small Design Automation Methodology

项目摘要

The overarching goal of this project is to develop highly adaptable deep-learning (DL) frameworks for real-time applications, such as autonomous vehicles and mobile health. To this aim, the proposed design-automation methodology will bridge runtime system optimization with advanced DL model architectures, through leveraging the techniques of split computing (SC) and early-exit computation (EEC). In traditional methodologies, the design process of a DL model is performed in isolation, where its structure is optimized with respect to a dataset and fixed system conditions. Instead, the new frameworks will jointly use SC and EEC to build DL models specifically designed to adapt real-time data analysis to time-varying characteristics of the system (e.g., available energy, computing power, channel capacity, computing task, etc.) and the information stream. To accomplish this objective, the team will use tools such as deep reinforcement learning and neural architecture search. The project will include a well-laid out educational and outreach plan, in which the involvement of undergraduate and graduate students is particularly promising. The project also proposes a suite of university initiatives which they will leverage to enhance diversity during the execution of this project.The research endeavor will produce frameworks that are expected to considerably boost the performance of critical applications such as autonomous vehicles and AI-empowered monitoring for mobile health while reducing their energy consumption and wireless channel usage. Software and simulation artifacts will be released as open-source platforms to enhance research in this important area.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.

该项目的总体目标是为实时应用开发高度适应性的深度学习（DL）框架，例如自动驾驶汽车和移动的健康。为此，建议的设计自动化方法将桥梁运行时系统优化与先进的DL模型架构，通过利用技术的分裂计算（SC）和早期退出计算（EEC）。在传统方法中，DL模型的设计过程是孤立执行的，其中其结构是相对于数据集和固定的系统条件进行优化的。相反，新框架将联合使用SC和EEC来构建DL模型，这些模型专门用于使实时数据分析适应系统的时变特性（例如，可用能量、计算能力、信道容量、计算任务等）和信息流。为了实现这一目标，该团队将使用深度强化学习和神经架构搜索等工具。该项目将包括一个精心设计的教育和推广计划，其中本科生和研究生的参与特别有希望。该项目还提出了一套大学倡议，他们将利用这些倡议在项目执行期间增强多样性。该研究奋进将产生框架，预计将大大提高关键应用的性能，如自动驾驶汽车和人工智能支持的移动的健康监测，同时减少能源消耗和无线信道使用。软件和仿真工件将作为开源平台发布，以加强这一重要领域的研究。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

期刊论文数量（19）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

DOMINO: Domain-Invariant Hyperdimensional Classification for Multi-Sensor Time Series Data

DOI：
10.1109/iccad57390.2023.10323848
发表时间：
2023-08
期刊：
2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD)
影响因子：
0
作者：
Junyao Wang;Luke Chen;M. A. Faruque
通讯作者：
Junyao Wang;Luke Chen;M. A. Faruque

Template Matching Based Early Exit CNN for Energy-efficient Myocardial Infarction Detection on Low-power Wearable Devices

基于模板匹配的早期退出 CNN，用于低功耗可穿戴设备上的节能心肌梗塞检测

DOI：
10.1145/3534580
发表时间：
2022
期刊：
Wearable and Ubiquitous Technologies
影响因子：
0
作者：
Rashid, Nafiul;Demirel, Berken Utku;Odema, Mohanad;Al Faruque, Mohammad Abdullah
通讯作者：
Al Faruque, Mohammad Abdullah

Stress Detection Using Context-Aware Sensor Fusion From Wearable Devices

使用可穿戴设备的上下文感知传感器融合进行压力检测

DOI：
10.1109/jiot.2023.3265768
发表时间：
2023
期刊：
IEEE Internet of Things Journal
影响因子：
10.6
作者：
Rashid, Nafiul;Mortlock, Trier;Faruque, Mohammad Abdullah
通讯作者：
Faruque, Mohammad Abdullah

PrivyNAS: Privacy-Aware Neural Architecture Search for Split Computing in Edge-Cloud Systems

DOI：
10.1109/jiot.2023.3311761
发表时间：
2024-02-15
期刊：
IEEE INTERNET OF THINGS JOURNAL
影响因子：
10.6
作者：
Odema，Mohanad;Al Faruque，Mohammad Abdullah
通讯作者：
Al Faruque，Mohammad Abdullah

Context-Aware Heterogeneous Task Scheduling for Multi-Layered Systems

DOI：
10.1109/wowmom57956.2023.00034
发表时间：
2023-06
期刊：
2023 IEEE 24th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)
影响因子：
0
作者：
Sharon L. G. Contreras;M. Levorato
通讯作者：
Sharon L. G. Contreras;M. Levorato