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

最近，由于理解关系的广泛需求，图形处理引起了人们的浓厚兴趣。图分析广泛应用于我们社会的关键领域，如网络安全、社交媒体、基础设施监控(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快速增长的计算机科学和工程研究领域。另一方面，在许多新兴应用中，图分析理想地在边缘(例如，移动或嵌入式系统)中执行，以便允许在事件展开的领域中发现事件之间的关系。遗憾的是，现有的配备CPU/GPU等传统计算单元的嵌入式系统不能高效地实时处理大型图形。相反，大型数据中心需要执行图形处理，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。本研究旨在利用颠覆性的新兴技术，在嵌入式系统中有效地实现图形分析。为了在嵌入式系统有限的硬件资源上支持图形分析应用，本项目致力于开发GAMBIT--一种基于忆阻器的嵌入式计算框架，用于高效的图形处理。我们的研究计划旨在开发多层技术，以在嵌入式系统(即网络边缘)中实现高效(例如，1000倍)和可扩展的实时图形分析。它包含了跨电路、架构、系统和垂直集成的研究成果。(1)在电路级，提出了一种基于忆阻器的图形计算核，以实现图形处理的高效计算。(2)在体系结构层面，提出了完整的基于忆阻器的分区图处理结构和各种算法。(3)在系统级，开发了嵌入式系统的图形分析框架，并将其与流行的嵌入式操作系统进行了集成。(4)在集成方面，该项目建议开发所提出的体系结构和跨层软硬件协同设计技术的仿真器。该项目通过吸引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，利用嵌入式系统中的图形处理和其他新兴应用程序扩展计算机工程课程，传播用于教育和培训的研究基础设施，以及与业界合作，为社会做出贡献。

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.