EAGER: Data-Mining Driven Power-Efficient Intelligent Memory Storage for Mobile Video Applications

EAGER：适用于移动视频应用的数据挖掘驱动型节能智能内存存储

• 批准号: 1514780
• 负责人: Na Gong
• 金额: $ 20万
• 依托单位: North Dakota State University Fargo
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-07-01 至 2018-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1514780&HistoricalAwards=false
• 关键词: EAGER; Data; Mining; Driven; Power

Mobile devices such as smart-phones and tablets have become the most important medium for delivering Internet traffic, especially multimedia content, to end users. One of the most popular multimedia applications is video streaming. During this process, video decoding has become the dominant energy-intensive application used in mobile devices. In particular, the major signal processing units in video decoders, such as motion estimation and compensation, forward and inverse discrete cosine transform, require a significant amount of calculations and frequent embedded memory accesses. It is understood that embedded SRAM consumes a large amount of power and limits battery life, and this situation is only expected to grow with the emerging popularity of high quality mobile video applications. This project proposes to address this problem by incorporating advanced data mining techniques particularly suited to mobile video data applications into the hardware design process to yield an intelligent memory having high power efficiency. The PIs will explore and characterize the behaviors of video data and provide a better-informed low power hardware design. The goal is to create new power efficient mobile video memory designs that utilize the identified characteristics extracted by suitable data-mining techniques tailored to video data, which will serve as a core foundation to bring about drastic improvements in energy efficiency. The exploration of these intelligent low power techniques through the interaction of both hardware and software viewpoints will enable a new dimension for power savings. The success of this project will have a huge impact on the mobile computing community, architecture community, and everyday life. This project will also serve as an excellent educational platform to improve the understanding of green computing amongst future computer scientists and computer engineers. The PIs will jointly develop course modules focusing on software/hardware co-design for mobile devices, which can be integrated into a variety of different courses. The PIs will also continue to recruit underrepresented students, such as females and minorities, to participate in this project.

诸如智能电话和平板电脑的移动的设备已经成为用于向终端用户递送互联网流量（尤其是多媒体内容）的最重要的介质。最流行的多媒体应用之一是视频流。在此过程中，视频解码已经成为移动的设备中使用的主要的能量密集型应用。特别是视频解码器中的主要信号处理单元，如运动估计和补偿、正向和反向离散余弦变换，需要大量的计算和频繁的嵌入式存储器访问。据了解，嵌入式SRAM消耗大量功率并限制了电池寿命，而这种情况只会随着高质量移动的视频应用的新兴普及而增长。 该项目提出通过将特别适合于移动的视频数据应用的先进数据挖掘技术结合到硬件设计过程中来解决这个问题，以产生具有高功率效率的智能存储器。PI将探索和表征视频数据的行为，并提供更好的低功耗硬件设计。我们的目标是创建新的高能效移动的视频存储器设计，该设计利用通过针对视频数据量身定制的合适的数据挖掘技术提取的识别特征，这将作为实现能源效率大幅提高的核心基础。通过硬件和软件观点的交互来探索这些智能低功耗技术，将为节能提供一个新的维度。这个项目的成功将对移动的计算社区、架构社区和日常生活产生巨大的影响。 这个项目也将作为一个很好的教育平台，以提高未来的计算机科学家和计算机工程师对绿色计算的理解。PI将联合开发课程模块，重点是移动的设备的软件/硬件协同设计，可以集成到各种不同的课程中。公共教育机构还将继续招收代表性不足的学生，如女性和少数民族学生参加这一项目。