Collaborative Research: CNS Core: Medium: Parallel and Real-Time Multicore Scheduling for an Efficiently-Used Cache (PARSEC)

合作研究:CNS 核心:中:高效使用缓存的并行实时多核调度 (PARSEC)

基本信息

  • 批准号:
    2211642
  • 负责人:
  • 金额:
    $ 27.5万
  • 依托单位:
  • 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:
    2022-09-01 至 2022-12-31
  • 项目状态:
    已结题

项目摘要

Safety-critical systems that have strict “real-time” requirements are becoming increasingly ubiquitous and complex. Epitomizing this recent trend toward sophisticated real-time systems are autonomous vehicles, which must perform image recognition, machine learning, routing, and planning tasks, simultaneously and with minimal delay. Furthermore, these real-time computational tasks must execute upon shared hardware (e.g., processors, memory, storage) due to the severe constraints on the size, weight, and power of the entire system; however, the sharing of computer resources creates tremendous contention and competition between tasks. This project addresses a fundamental challenge of how multiple real-time, safety-critical tasks can effectively share the underlying memory architecture and still meet timing constraints. In particular, this project will develop a novel system design and analysis framework called PARSEC (Parallel and Real-Time Multicore Scheduling for an Efficiently-Used Cache). PARSEC contributes to the state-of-the-art with (a) new multicore scheduling algorithms that explicitly manage how contending tasks share memory resources; (b) new formal analysis techniques that verify that a system’s timing constraints are satisfied with existing memory resources; and (c) a set of open-source automated tools that will enable system designers to utilize the framework on commercial off-the-shelf processing architectures. PARSEC will be implemented and evaluated upon the popular RISC V architecture to facilitate wide dissemination to the public.This project will result in safer, more efficient designs of time-sensitive systems, including autonomous vehicles and robotics. Furthermore, the resulting research and system design techniques in this project can be applied to any real-time, safety-critical systems executing concurrent computational tasks upon a shared processor and memory. The reduction in contention in the memory hierarchy obtained from project artifacts will potentially lessen demands on power and fuel in safety-critical systems, decreasing their carbon footprint. The project will benefit the educational missions of University of Nevada Las Vegas and Wayne State University by providing a unique training, education, and experiential learning opportunity for undergraduate and graduate students via course projects related to safety-critical system design. To aid other researchers, this project will also disseminate research results through publications, public talks, tutorials, project websites, and online videos.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.
具有严格“实时”要求的安全关键系统正变得越来越普遍和复杂。最近这种复杂的实时系统趋势的缩影是自动驾驶汽车,它必须同时执行图像识别,机器学习,路由和规划任务,并且延迟最小。此外,这些实时计算任务必须在共享硬件上执行(例如,处理器、存储器、存储器);然而,计算机资源的共享在任务之间产生了巨大的争用和竞争。该项目解决了一个根本性的挑战,即多个实时,安全关键的任务如何有效地共享底层内存架构,并仍然满足时间限制。特别是,该项目将开发一个新的系统设计和分析框架,称为PARSEC(并行和实时多核调度的高效使用的缓存)。PARSEC对最先进的技术做出了贡献:(a)新的多核调度算法,明确管理竞争任务如何共享内存资源;(B)新的形式化分析技术,验证系统的时序约束是否满足现有内存资源;(c)一组开源自动化工具,使系统设计人员能够在商业现成的处理架构上利用框架。PARSEC将在流行的RISC V架构上实施和评估,以促进向公众的广泛传播。该项目将导致更安全,更有效的时间敏感系统设计,包括自动驾驶汽车和机器人。此外,在这个项目中所产生的研究和系统设计技术可以应用到任何实时,安全关键的系统上执行并发计算任务的共享处理器和存储器。从项目工件中获得的内存层次结构中的争用的减少将潜在地减少安全关键系统中对电力和燃料的需求,从而减少其碳足迹。该项目将有利于内华达州大学拉斯维加斯和韦恩州立大学的教育使命,通过与安全关键系统设计相关的课程项目为本科生和研究生提供独特的培训,教育和体验式学习机会。为了帮助其他研究人员,该项目还将通过出版物、公开讲座、教程、项目网站和在线视频传播研究成果。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(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

Abusayeed Saifullah其他文献

Demo Abstract: Implementing SNOW on Commercial Off-The-Shelf Devices
演示摘要:在商用现成设备上实现 SNOW
All Theses and Dissertations ( ETDs ) January 2011 Empirical Studies for Reliable Home Area Wireless Sensor Networks
所有论文 (ETD) 2011 年 1 月 可靠家庭区域无线传感器网络的实证研究
  • DOI:
  • 发表时间:
    2017
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Mo Sha;Chenyang Lu;Yixin Chen;Christopher Gill;Greg Hackmann;Chengjie Wu;Sisu Xi;Yong Fu;Bo Li;Abusayeed Saifullah
  • 通讯作者:
    Abusayeed Saifullah
Correction of an Augmentation Bound Analysis for Parallel Real-Time Tasks
并行实时任务的增强界限分析的修正
  • DOI:
    10.7936/k7s75dk8
  • 发表时间:
    2012
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Abusayeed Saifullah;Kunal Agrawal;Chenyang Lu;C. Gill
  • 通讯作者:
    C. Gill
Empirical Studies for Reliable Home Area Wireless Sensor Empirical Studies for Reliable Home Area Wireless Sensor Networks Networks
可靠家庭区域无线传感器的实证研究 可靠家庭区域无线传感器网络的实证研究
  • DOI:
  • 发表时间:
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Mo Sha;Professor Chenyang Lu;Greg Hackmann;Chengjie Wu;Sisu Xi;Yong Fu;Bo Li;Abusayeed Saifullah;Rahav Dor
  • 通讯作者:
    Rahav Dor
Number : WUCSE-2013-25 2013 Parallel Real-Time Scheduling of DAGs
编号:WUCSE-2013-25 2013 DAG 并行实时调度
  • DOI:
  • 发表时间:
    2015
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Abusayeed Saifullah;D. Ferry;Jing Li;Kunal Agrawal;Chenyang Lu
  • 通讯作者:
    Chenyang Lu

Abusayeed Saifullah的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('Abusayeed Saifullah', 18)}}的其他基金

CAREER: Protocols for Low-Power Wide-Area Networks in White Spaces
职业:空白区域低功耗广域网协议
  • 批准号:
    2306486
  • 财政年份:
    2022
  • 资助金额:
    $ 27.5万
  • 项目类别:
    Standard Grant
CNS Core: Small: Low-Power Wide-Area Networks for Industrial Automation
CNS 核心:小型:用于工业自动化的低功耗广域网
  • 批准号:
    2301757
  • 财政年份:
    2022
  • 资助金额:
    $ 27.5万
  • 项目类别:
    Standard Grant
Collaborative Research: CNS Core: Medium: Parallel and Real-Time Multicore Scheduling for an Efficiently-Used Cache (PARSEC)
合作研究:CNS 核心:中:高效使用缓存的并行实时多核调度 (PARSEC)
  • 批准号:
    2306745
  • 财政年份:
    2022
  • 资助金额:
    $ 27.5万
  • 项目类别:
    Continuing Grant
CAREER: Protocols for Low-Power Wide-Area Networks in White Spaces
职业:空白区域低功耗广域网协议
  • 批准号:
    2211523
  • 财政年份:
    2021
  • 资助金额:
    $ 27.5万
  • 项目类别:
    Standard Grant
CNS Core: Small: Low-Power Wide-Area Networks for Industrial Automation
CNS 核心:小型:用于工业自动化的低功耗广域网
  • 批准号:
    2211510
  • 财政年份:
    2021
  • 资助金额:
    $ 27.5万
  • 项目类别:
    Standard Grant
CNS Core: Small: Low-Power Wide-Area Networks for Industrial Automation
CNS 核心:小型:用于工业自动化的低功耗广域网
  • 批准号:
    2006467
  • 财政年份:
    2020
  • 资助金额:
    $ 27.5万
  • 项目类别:
    Standard Grant
CAREER: Protocols for Low-Power Wide-Area Networks in White Spaces
职业:空白区域低功耗广域网协议
  • 批准号:
    1846126
  • 财政年份:
    2019
  • 资助金额:
    $ 27.5万
  • 项目类别:
    Standard Grant
CRII: NeTS: Towards the Design of a Large-Scale Wireless Sensor Network
CRII:NeTS:面向大规模无线传感器网络的设计
  • 批准号:
    1742985
  • 财政年份:
    2017
  • 资助金额:
    $ 27.5万
  • 项目类别:
    Standard Grant
CRII: NeTS: Towards the Design of a Large-Scale Wireless Sensor Network
CRII:NeTS:面向大规模无线传感器网络的设计
  • 批准号:
    1565751
  • 财政年份:
    2016
  • 资助金额:
    $ 27.5万
  • 项目类别:
    Standard Grant

相似国自然基金

Research on Quantum Field Theory without a Lagrangian Description
  • 批准号:
    24ZR1403900
  • 批准年份:
    2024
  • 资助金额:
    0.0 万元
  • 项目类别:
    省市级项目
Cell Research
  • 批准号:
    31224802
  • 批准年份:
    2012
  • 资助金额:
    24.0 万元
  • 项目类别:
    专项基金项目
Cell Research
  • 批准号:
    31024804
  • 批准年份:
    2010
  • 资助金额:
    24.0 万元
  • 项目类别:
    专项基金项目
Cell Research (细胞研究)
  • 批准号:
    30824808
  • 批准年份:
    2008
  • 资助金额:
    24.0 万元
  • 项目类别:
    专项基金项目
Research on the Rapid Growth Mechanism of KDP Crystal
  • 批准号:
    10774081
  • 批准年份:
    2007
  • 资助金额:
    45.0 万元
  • 项目类别:
    面上项目

相似海外基金

Collaborative Research: CNS Core: Medium: Reconfigurable Kernel Datapaths with Adaptive Optimizations
协作研究:CNS 核心:中:具有自适应优化的可重构内核数据路径
  • 批准号:
    2345339
  • 财政年份:
    2023
  • 资助金额:
    $ 27.5万
  • 项目类别:
    Standard Grant
Collaborative Research: CNS Core: Small: A Compilation System for Mapping Deep Learning Models to Tensorized Instructions (DELITE)
合作研究:CNS Core:Small:将深度学习模型映射到张量化指令的编译系统(DELITE)
  • 批准号:
    2230945
  • 财政年份:
    2023
  • 资助金额:
    $ 27.5万
  • 项目类别:
    Standard Grant
Collaborative Research: NSF-AoF: CNS Core: Small: Towards Scalable and Al-based Solutions for Beyond-5G Radio Access Networks
合作研究:NSF-AoF:CNS 核心:小型:面向超 5G 无线接入网络的可扩展和基于人工智能的解决方案
  • 批准号:
    2225578
  • 财政年份:
    2023
  • 资助金额:
    $ 27.5万
  • 项目类别:
    Standard Grant
Collaborative Research: CNS Core: Medium: Movement of Computation and Data in Splitkernel-disaggregated, Data-intensive Systems
合作研究:CNS 核心:媒介:Splitkernel 分解的数据密集型系统中的计算和数据移动
  • 批准号:
    2406598
  • 财政年份:
    2023
  • 资助金额:
    $ 27.5万
  • 项目类别:
    Continuing Grant
Collaborative Research: CNS Core: Small: SmartSight: an AI-Based Computing Platform to Assist Blind and Visually Impaired People
合作研究:中枢神经系统核心:小型:SmartSight:基于人工智能的计算平台,帮助盲人和视障人士
  • 批准号:
    2418188
  • 财政年份:
    2023
  • 资助金额:
    $ 27.5万
  • 项目类别:
    Standard Grant
Collaborative Research: CNS Core: Small: Creating An Extensible Internet Through Interposition
合作研究:CNS核心:小:通过介入创建可扩展的互联网
  • 批准号:
    2242503
  • 财政年份:
    2023
  • 资助金额:
    $ 27.5万
  • 项目类别:
    Standard Grant
Collaborative Research: CNS Core: Small: Adaptive Smart Surfaces for Wireless Channel Morphing to Enable Full Multiplexing and Multi-user Gains
合作研究:CNS 核心:小型:用于无线信道变形的自适应智能表面,以实现完全复用和多用户增益
  • 批准号:
    2343959
  • 财政年份:
    2023
  • 资助金额:
    $ 27.5万
  • 项目类别:
    Standard Grant
Collaborative Research: CNS Core: Small: Efficient Ways to Enlarge Practical DNA Storage Capacity by Integrating Bio-Computer Technologies
合作研究:中枢神经系统核心:小型:通过集成生物计算机技术扩大实用 DNA 存储容量的有效方法
  • 批准号:
    2343863
  • 财政年份:
    2023
  • 资助金额:
    $ 27.5万
  • 项目类别:
    Standard Grant
Collaborative Research: CNS Core: Small: A Compilation System for Mapping Deep Learning Models to Tensorized Instructions (DELITE)
合作研究:CNS Core:Small:将深度学习模型映射到张量化指令的编译系统(DELITE)
  • 批准号:
    2341378
  • 财政年份:
    2023
  • 资助金额:
    $ 27.5万
  • 项目类别:
    Standard Grant
Collaborative Research: CNS Core: Medium: Innovating Volumetric Video Streaming with Motion Forecasting, Intelligent Upsampling, and QoE Modeling
合作研究:CNS 核心:中:通过运动预测、智能上采样和 QoE 建模创新体积视频流
  • 批准号:
    2409008
  • 财政年份:
    2023
  • 资助金额:
    $ 27.5万
  • 项目类别:
    Continuing Grant
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了