CAREER: Enabling Memory-Centric Computing from Internet of Things to Cloud

职业：实现从物联网到云的以内存为中心的计算

• 批准号: 2339317
• 负责人: Elaheh Sadredini
• 金额: $ 58.76万
• 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: 2024
• 资助国家: 美国
• 起止时间: 2024-04-01 至 2029-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2339317&HistoricalAwards=false
• 关键词: CAREER; Enabling; Memory; Centric; Computing

Today's computing landscape is predominantly driven by data-centric applications, where their performance heavily relies on efficient data access and processing. Memory-centric computing revolutionizes data processing by performing computations in close proximity to the locations where data is generated/captured or stored, such as sensors, memory, and storage devices, and capitalizes on the extensive parallelism inherent in memory arrays. Therefore, it fundamentally tackles the data movement and instruction processing bottleneck, offering a powerful solution for data-intensive workloads in domains like machine learning and genomics, with far-reaching implications for scientific advancements. The overarching goal of this project is to facilitate the paradigm shift from compute-centric to memory-centric systems and seamlessly and securely integrate them into today's modern systems. This transformation will enable efficient and secure computation, fostering advancements in data processing capabilities. This project aims to integrate the acquired methodologies and insights into age-appropriate curricula, from K-12 to graduate-level courses. It also incorporates several STEM (science, technology, engineering and mathematics) enrichment activities for local K-12 and community college students in the Inland Empire.This award aims to facilitate the integration of the memory-centric computing paradigm into present-day computing systems. To fully exploit the potential of memory-centric computing, this project takes a holistic approach by co-designing the entire computing stack to facilitate the adoption of near-data processing in modern architectures. It will also investigate the security and privacy challenges posed by memory-centric computing solutions and propose lightweight techniques to effectively address these challenges. The developed frameworks and tools will be made publicly available to benefit the computer science community and to serve as the foundation for new course development and creating research opportunities for undergraduate and high school students.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.

当今的计算环境主要由以数据为中心的应用程序驱动，这些应用程序的性能严重依赖于高效的数据访问和处理。以存储器为中心的计算通过在生成/捕获或存储数据的位置（诸如传感器、存储器和存储设备）附近执行计算来彻底改变数据处理，并且利用存储器阵列中固有的广泛并行性。因此，它从根本上解决了数据移动和指令处理瓶颈，为机器学习和基因组学等领域的数据密集型工作负载提供了强大的解决方案，对科学进步产生了深远的影响。该项目的首要目标是促进从以计算为中心的系统到以内存为中心的系统的范式转变，并将其无缝安全地集成到当今的现代系统中。这种转变将实现高效和安全的计算，促进数据处理能力的进步。该项目旨在将获得的方法和见解整合到适合年龄的课程中，从K-12到研究生课程。该奖项还为内陆帝国的当地K-12和社区大学学生提供了几项STEM（科学，技术，工程和数学）丰富活动。该奖项旨在促进以内存为中心的计算范式融入当今的计算系统。为了充分利用以内存为中心的计算的潜力，该项目采取了一种整体方法，通过共同设计整个计算堆栈，以促进在现代架构中采用近数据处理。它还将调查以内存为中心的计算解决方案所带来的安全和隐私挑战，并提出轻量级技术来有效地解决这些挑战。开发的框架和工具将公开提供，以造福计算机科学界，并作为新课程开发的基础，为本科生和高中生创造研究机会。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。