Workshop Proposal: Processing-In-Memory (PIM) Technology - Grand Challenges and Applications

研讨会提案：内存处理 (PIM) 技术 - 重大挑战和应用

• 批准号: 2027324
• 负责人: Yiran Chen
• 金额: $ 5万
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2021-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2027324&HistoricalAwards=false
• 关键词: Workshop; Proposal; Processing; Memory; PIM

Processing-In-Memory (PIM) is a promising technology that was proposed to eliminate the costly data transmission between memory and computing units in conventional computer architectures. An input data can be directly injected into the memory that stores a constant for a computation without loading the constant to the computing unit. Such a design is particularly suitable for many emerging computations, such as graph analytics and neuromorphic computing, where one of the operands is constant during the computation while only the input changes. By eliminating the data transmission between the memory and computing units, PIM can substantially improve the computational efficiency of these applications.This project organizes a workshop to gather leading researchers and experts who have spent considerable effort and time studying Processing-In-Memory (PIM) technology. The proposed workshop will provide a forum for leading experts to consider in a synergistic manner a view of PIM technology from the perspective of circuits, architectures, systems, and applications. These researchers will discuss their visions of the critical challenges that need to be addressed in the near future. With the help of the participants, the workshop will produce a comprehensive report on the current state of the art and the outlook of PIM technology.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.

内存处理（PIM）是一项很有前途的技术，旨在消除传统计算机架构中内存和计算单元之间昂贵的数据传输。输入数据可以直接注入到存储常量的存储器中以进行计算，而不需要将常量加载到计算单元。这种设计特别适合许多新兴计算，例如图分析和神经形态计算，其中一个操作数在计算过程中保持不变，而只有输入发生变化。通过消除内存和计算单元之间的数据传输，PIM 可以大幅提高这些应用程序的计算效率。该项目组织了一个研讨会，聚集了花费大量精力和时间研究内存处理（PIM）的领先研究人员和专家。技术。拟议的研讨会将为顶尖专家提供一个论坛，让他们从电路、架构、系统和应用的角度以协同方式考虑 PIM 技术。 这些研究人员将讨论他们对不久的将来需要解决的关键挑战的看法。在参与者的帮助下，研讨会将就 PIM 技术的现状和前景提供一份全面的报告。该奖项反映了 NSF 的法定使命，并通过利用基金会的智力优势和更广泛的评估进行评估，认为值得支持。影响审查标准。