CRII: SHF: A Memory-Centric Hardware Accelerator for Large Scale Data Clustering

在几乎所有科学和工程学科中，集群都是分析数据的重要工具。美国国家科学院(NAS)最近宣布了统计数据分析的七大巨头，数据集群在其中发挥着核心作用。这份报告还强调，需要更具可扩展性的解决方案来支持未来大规模数据分析的时间和空间集群。因此，为了使未来大规模数据分析实用化，需要能够显著提高数据聚类技术的能效和性能的硬件和软件创新。为了实现这一目标，该研究展示了一种完全不同的未来解决数据集群问题的愿景，大规模的集群问题被映射到以存储器为中心的非冯·诺伊曼计算基板上，并在数据阵列内原位求解，其性能和能效比现代计算机系统高出数量级。该项目将利用阻性随机存取存储器(RRAM)的最新发展和用于在大规模并行框架(如位序列秩序滤波器)内重新制定集群问题的算法方法，为未来的集群应用构建极低功耗和快速的存储器基板。在软件层面，将开发新的算法，将不同类型的不同类型的数据集群问题(包括数值和非数值数据点)从科学和工程领域映射到拟议的记忆加速器上。将开发用于软硬件协同设计、动态资源管理和内存分配的编程模型、软件模块和应用程序库，以使用户能够在运行时控制数据集群过程。在硬件层面，我们将研究优化由新型电阻单元和互连网络构建的存储模块的功率和性能的技术。将通过发表的论文以及关于非冯·诺伊曼计算机系统中新出现的现场计算平台和软硬件接口的教程，向更广泛的研究界传播架构和软件创新。该项目的教育部分将涉及将单元结构、互连网络、分层软件接口和控制策略整合到高级计算机体系结构课程的教学大纲中。

Clustering is a crucial tool for analyzing data in virtually every scientific and engineering discipline. The U.S. National Academy of Sciences (NAS) has recently announced "the seven giants of statistical data analysis" in which data clustering plays a central role. This report also emphasizes that more scalable solutions are required to enable time and space clustering for the future large scale data analyses. As a result, hardware and software innovations that can significantly improve energy-efficiency and performance of the data clustering techniques are necessary to make the future large scale data analysis practical. To this goal, the proposed research demonstrates a radically different vision of solving data clustering problems in the future, where large-scale clustering problems are mapped onto a memory-centric, non-Von Neumann computation substrate and solved in situ within the data arrays, with orders of magnitude greater performance and energy efficiency than contemporary computer systems.The proposed project will leverage recent developments in resistive random access memory (RRAM) and algorithmic approaches for reformulating clustering problems within massively parallel frameworks, such as bit serial rank order filters, to build an extremely low power and fast memory substrate for future clustering applications. At the software level, novel algorithms will be developed to map different types of heterogeneous data clustering problems (including numerical and non-numerical data points) from scientific and engineering domains onto the proposed memristive accelerator. Programming models, software modules, and application libraries for hardware-software co-design, dynamic resource management, and memory allocation will be developed to give the user control of the data clustering process at runtime. At the hardware level, we will investigate techniques for optimizing power and performance of the memory modules constructed from novel resistive cells and interconnection networks. Architecture and software innovations will be disseminated to the broader research community through published papers, as well as tutorials on the emerging in situ computing platforms and software-hardware interfaces in non-Von Neumann computer systems. The educational component of this project will involve integrating the cell structure, the interconnection networks, the hierarchical software interface, and the control policies into the syllabus of an advanced computer architecture course.