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.

嵌入式系统依靠具有严格尺寸，重量和功率限制的嵌入式硬件，在现代世界中起着至关重要的作用。这种系统的资源受限性质具有众所周知的记忆墙问题，从而影响了容量和潜伏期。为了应对这一挑战，该项目旨在建立一个称为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中的内存使用情况，并探索跨堆栈优化的机会。 MEMDRIVE涵盖了三个关键创新：（i）在应用程序堆栈中，将设计跨任务优化技术，以共享不同任务的中间结果，而不会损害性能。 （ii）将在系统堆栈，内存和资源管理方案中实现，该方案可以包含集成物理内存的硬件趋势，并进一步减少多个过程中的内存冗余。 （iii）将探索跨堆栈协作，以通过利用应用程序堆栈属性（例如任务之间的共享中间结果和可预测的内存使用模式）来进一步提高内存使用效率和延迟。将在一组机器人测试床上运行现实世界应用的广泛实验。这项研究有可能为开发以前受到内存约束限制的新型应用开发新的可能性。由于嵌入式系统在机器人技术和物联网等各个领域中起着越来越重要的作用，因此项目的结果将带来改善的用户体验，并通过使低成本嵌入式系统产品更易于访问，从而产生巨大的社会影响。此外，该项目旨在培养熟练的工程师和计算机科学家的管道，并在嵌入式系统中具有跨学科的专业知识。将开发研究生和本科课程模块，使马萨诸塞州和加利福尼亚州所有工程学科的学生受益。将通过多元化计划和K-12学生的宣传计划招募代表性不足的学生的努力。这种包容性将为嵌入式系统领域的研究人员和创新者组成的多样化和动态的社区。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的评估评估的评估来支持的。