CAREER: Centaur: A Bio-inspired Ultra Low-Power Hybrid Embedded Computing Engine Beyond One TeraFlops/Watt

职业：Centaur：超越 1 TeraFlops/W 的仿生超低功耗混合嵌入式计算引擎

• 批准号: 1744111
• 负责人: Yiran Chen
• 金额: $ 24.66万
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-05-01 至 2018-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1744111&HistoricalAwards=false
• 关键词: CAREER; Centaur; Bio; inspired; Ultra

The objective of the research is to innovate an embedded computing engine named ?Centaur? to achieve ultra-high power efficiency by adopting the bio-inspired computation model and the advanced memristor technology. Three constituent elements are included to address the major technical obstacles: (1) The power-efficient hybrid computing system that integrates memristor-based synapse network and crossbar structure, targeting the flexible and intensive data processing, respectively. (2) The robust design methodology for Centaur, including the circuit and algorithm enhancements as well as the necessary EDA flow. (3) The integration of Centaur into modern heterogeneous systems and the prototype demonstration. Creative applications of critical importance to nowadays mobile and embedded systems by taking the full advantages of Centaur, including pattern recognition and video and image processing, will be also explored.The research can benefit the embedded system community by the revolutions in computing architecture and hardware design for functional variety, power-efficiency, and cost. The results can further benefit the semiconductor and neuromorphic societies at large by stimulating the interaction between the advances in device engineering and computing models. The developed techniques will be transferred to mainstream practices under the close collaborations with several industry partners, and directly impact the future embedded systems. The activities in the collaboration also include the tutorials in the major conferences on the technical aspects of the projects and new course development. The educational plan will enhance the existing standard curricula by integrating new modules on emerging memristor-based computing architecture and the relevant neuromorphic computing model.

该研究的目的是创新的嵌入式计算引擎命名？半人马？采用仿生计算模型和先进的忆阻器技术，实现超高的功率效率。主要技术障碍包括三个组成要素：（1）集成基于忆阻器的突触网络和交叉开关结构的高能效混合计算系统，分别针对灵活和密集的数据处理。(2)Centaur的稳健设计方法，包括电路和算法增强以及必要的EDA流程。(3)Centaur与现代异构系统的集成及原型演示。此外，本研究亦将探讨Centaur的创新应用，包括模式识别、视频及图像处理等，这些创新应用对现今移动的及嵌入式系统至关重要。本研究将有助推动嵌入式系统在计算架构及硬件设计方面的革新，以提升功能多样性、节能性及成本效益。这些结果可以通过刺激设备工程和计算模型的进步之间的相互作用，进一步造福于半导体和神经形态社会。开发的技术将在与多个行业合作伙伴的密切合作下转移到主流实践中，并直接影响未来的嵌入式系统。合作的活动还包括在主要会议上就项目的技术方面和新课程开发提供辅导。该教育计划将通过整合关于新兴的基于忆阻器的计算架构和相关的神经形态计算模型的新模块来加强现有的标准课程。