Collaborative Research: CSR: Medium: MemDrive: Memory-Driven Full-Stack Collaboration for Autonomous Embedded Systems

协作研究：CSR：媒介：MemDrive：自主嵌入式系统的内存驱动全栈协作

• 批准号: 2312397
• 负责人: Cong Liu
• 金额: $ 33.3万
• 依托单位: University of California-Riverside
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2312397&HistoricalAwards=false
• 关键词: Collaborative; Research; CSR; Medium; MemDrive

Embedded systems, relying on embedded hardware with stringent size, weight, and power constraints, play a crucial role in the modern world. The resource-constrained nature of such systems has the well-known memory wall problem, impacting both capacity and latency. To address this challenge, this project aims to establish a comprehensive memory management ecosystem, called MemDrive, to optimize memory usage throughout the application and system stack and explore opportunities for cross-stack optimization. MemDrive encompasses three key innovations: (i) At the application stack, cross-task optimization techniques will be designed to share intermediate results across different tasks without compromising performance. (ii) At the system stack, memory and resource management schemes will be implemented that could embrace the hardware trend of integrated physical memory and further reduce memory redundancy across multiple processes. (iii) Cross-stack collaboration will be explored to further improve memory usage efficiency and latency by leveraging application-stack properties, such as shared intermediate results among tasks and predictable memory usage patterns. Extensive experiments for running real-world applications on top of a set of robotic testbeds will be conducted.This research has the potential to open new possibilities for the development of novel applications that were previously limited by memory constraints. As embedded systems play an increasingly significant role in various domains, such as robotics and the Internet of Things, the project's outcomes will lead to improved user experiences and have a large social impact by making low-cost embedded systems products more accessible. Moreover, this project aims to cultivate a pipeline of skilled engineers and computer scientists with interdisciplinary expertise in embedded systems. Graduate and undergraduate course modules will be developed, benefiting students in all engineering disciplines in Massachusetts and California. Efforts will be made to recruit underrepresented students through diversity programs and outreach initiatives to K-12 students. This inclusiveness will contribute to a diverse and dynamic community of researchers and innovators in the field of embedded systems.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.

嵌入式系统依赖于具有严格尺寸、重量和功耗限制的嵌入式硬件，在现代世界中发挥着至关重要的作用。这种系统的资源受限性质具有众所周知的内存墙问题，影响容量和延迟。为了应对这一挑战，该项目旨在建立一个名为MemDrive的全面内存管理生态系统，以优化整个应用程序和系统堆栈的内存使用，并探索跨堆栈优化的机会。MemDrive包含三个关键创新：（i）在应用程序堆栈中，将设计跨任务优化技术，以便在不影响性能的情况下在不同任务之间共享中间结果。(ii)在系统堆栈中，将实施内存和资源管理方案，这些方案可以拥抱集成物理内存的硬件趋势，并进一步减少多个进程之间的内存冗余。(iii)将探索跨堆栈协作，以进一步提高内存使用效率和延迟，通过利用应用程序堆栈属性，如任务之间共享的中间结果和可预测的内存使用模式。将在一组机器人测试平台上运行真实世界的应用程序进行广泛的实验。这项研究有可能为开发以前受内存限制的新应用程序开辟新的可能性。随着嵌入式系统在机器人和物联网等各个领域发挥越来越重要的作用，该项目的成果将改善用户体验，并通过使低成本嵌入式系统产品更容易获得而产生巨大的社会影响。此外，该项目旨在培养一批具有嵌入式系统跨学科专业知识的熟练工程师和计算机科学家。将开发研究生和本科课程模块，使马萨诸塞州和加州所有工程学科的学生受益。将努力通过多样性方案和向K-12学生的外联活动招募代表性不足的学生。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。