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

像智能手机和平板电脑这样的移动设备已经成为向最终用户传递互联网流量，尤其是多媒体内容的最重要媒介。最流行的多媒体应用之一是视频流。在这一过程中，视频解码已经成为移动设备中主要的高能耗应用。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，正反离散余弦变换，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，限制电池寿命，随着高质量移动视频应用的日益普及，这种情况只会越来越严重。本项目建议通过将特别适合移动视频数据应用的先进数据挖掘技术整合到硬件设计过程中来解决这一问题，从而产生具有高能效的智能存储器。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。目标是创造新的节能移动视频存储器设计，利用通过适合视频数据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。通过硬件和软件视角的交互对这些智能低功耗技术的探索将为节能提供一个新的维度。这个项目的成功将对移动计算社区、架构社区和日常生活产生巨大的影响。这个项目也将成为一个很好的教育平台，让未来的计算机科学家和计算机工程师对绿色计算有更深入的了解。双方将共同开发以移动设备软硬件协同设计为重点的课程模块，这些模块可以集成到各种不同的课程中。私立学校也将继续招收代表性不足的学生，如女性和少数族裔学生参加这个项目。

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.