CSR: Small: Collaborative Research: GAMBIT: Efficient Graph Processing on a Memristor-based Embedded Computing Platform

最近，图形处理收到了强烈的兴趣，在广泛的需求，以了解关系。图分析广泛用于我们社会的关键领域，例如网络安全、社交媒体、基础设施监控（例如，智能建筑），自然语言处理，系统生物学，推荐系统。这些重要的应用都属于计算机科学和工程研究中快速增长的领域。另一方面，在许多新兴应用中，图分析理想地在边缘中执行（例如，移动的或嵌入式系统），以便允许在事件被展开的领域中发现事件之间的关系。不幸的是，现有的嵌入式系统配备了传统的计算单元，如CPU/GPU不能有效地处理大型图形在真实的时间。相反，需要大型数据中心来执行图形处理，要么由于数据通信而导致额外的延迟和能量，要么只提供取证（离线）图形分析。这项研究旨在通过颠覆性的新兴技术有效地实现嵌入式系统中的图形分析。为了在嵌入式系统中利用有限的硬件资源支持图形分析应用，本项目旨在开发GAMBIT --一种基于忆阻器的高效图形处理嵌入式计算框架。我们的研究计划旨在开发多层技术，以实现高效（例如，1000 X）和嵌入式系统中的可扩展实时图形分析（即，网络边缘）。它包含电路，架构，系统和垂直集成的研究工作。(1)在电路层面，该项目提出了一个基于忆阻器的图形计算核心，以实现图形处理的高效计算。(2)在架构层面，本项目提出了完整的基于忆阻器的分区图处理架构和各种算法。(3)在系统层面，本计画开发一个嵌入式系统的图形分析框架，并将其与一个流行的嵌入式操作系统整合。（4）在整合方面，本计画提出开发一个模拟器来模拟所提出的架构与跨层的软硬件协同设计技术。该项目通过以下方式为社会作出贡献：让少数群体服务机构的高中生和本科生参与研究，吸引妇女和代表性不足的群体接受研究生教育，扩大计算机工程课程，增加图形处理和嵌入式系统中的其他新兴应用，传播教育和培训研究基础设施，以及与业界合作。

Recently, graph processing received intensive interests in light of a wide range of needs to understand relationships. Graph analytics are widely used in key domains in our society, such as cyber security, social media, infrastructure monitoring (e.g., smart building), natural language processing, system biology, recommendation systems. These important applications all fall into fast-growing sectors in computer science and engineering research. On the other hand, in many emerging applications, the graph analytics are ideally performed in the edge (e.g., a mobile or embedded system) in order to allow the relationships between events to be discovered in the field where they are unfold. Unfortunately, the existing embedded systems equipped with conventional computing units like CPU/GPU cannot efficiently process large graphs in real time. Instead, large data centers are required to perform the graph processing, either incurring extra latency and energy due to data communication or only providing forensic (offline) graph analysis. This research aims to effectively enable graph analytics in embedded system with disruptive emerging technology. To support graph analytic applications with the limited hardware resources in embedded systems, this project seeks to develop GAMBIT -- a memristor-based embedded computing framework for efficient graph processing. Our research program aims to develop multi-layer techniques to enable highly efficient (e.g., 1000X) and scalable real-time graph analytics in embedded systems (i.e., network edge). It contains research efforts across circuit, architecture, system and vertical integration. (1) At the circuit level, the project proposes a memristor-based graph computing core to enable efficient computations for graph processing. (2) At the architecture level, the project proposes the complete memristor-based graph processing architecture for partitioned graph and various algorithms. (3) At the system level, the project develops a graph analytics framework for embedded systems and integrates it with a popular embedded OS. (4) For integration, the project proposes to develop an emulator of the proposed architecture and cross-layer HW/SW co-design techniques. This project contributes to society through engaging high-school and undergraduate students from minority-serving institutions into research, attracting women and under-represented groups into graduate education, expanding the computer engineering curriculum with graph processing and other emerging applications in embedded systems, disseminating research infrastructure for education and training, and collaborating with the industry.