CSR: Small: Memory System Optimizations to Enable Fast-Response Mobile Devices at Low Power

过去的十年见证了计算平台的范式转变。新兴的手持设备，如智能手机和平板电脑，已经成为日常使用中最常见的计算设备之一。能源消耗是影响移动手持设备发展的主要考虑因素之一，因为它决定了设备在电池供电下保持可用的持续时间。智能手机等设备的使用模式与笔记本电脑和台式电脑等传统计算设备有很大不同，用户对这些手持移动设备的期望也不同。例如，移动设备被期望至少在一天内保持在线状态，并且能够快速启动和响应。另一方面，笔记本/台式设备在大量使用的情况下表现良好，并且对快速启动和响应的期望没有那么严格。此外，在应用程序使用方面，移动系统倾向于具有低水平多任务处理的突发使用，而桌面系统则涉及具有高水平多任务处理的更连续的长时间运行的程序。该项目旨在优化移动设备中的存储系统，通过利用这些使用差异来实现快速响应时间，同时保持低功耗。它由三管齐下的方法组成：首先，通过开发使用记录程序分析移动用户的使用模式来减少活动内存占用大小。其次，开发智能纠错方案，在不影响数据完整性的情况下减少存储系统消耗的刷新能量。第三，通过利用新兴的非易失性存储器（NVM）技术和开发可以将关键数据保存在NVM中的数据分区技术来降低内存功耗。内存功耗仍然是移动平台电池寿命的主要限制因素之一。除了作为智能手机和平板电脑的一般用途外，高效移动存储系统还用于监视，汽车，环境，军事和生物医学等其他几个应用领域。在我们的建议中开发的技术也将对这些领域有用。使用记录和使用特征的基础设施将促进移动存储系统领域的其他研究。

The past decade has seen a paradigm shift in computing platforms. Emerging handheld devices such as Smartphones and Tablets have become one of the most common devices for computing in everyday use. Energy consumption is one of the prime considerations that influence the development of mobile hand-held devices, as it determines the duration for which the device remains usable on battery power. The usage patterns for devices such as smartphones are quite different from traditional computing devices such as laptops and desktops, and users have a different set of expectation from these handheld mobile devices. For example, mobile devices are expected to be always-on for at-least a day and to start-up and respond quickly. On the other hand, laptop/desktop devices are expected to perform well under heavy use, and the expectations of fast start-up and response are not as stringent. Furthermore, in terms of application usage, mobile systems tends to have burst usage with low-levels of multitasking, while desktop usage involves more continuous long-running programs with high-levels of multitasking. The project seeks to optimize the memory system in mobile devices to enable fast-response time while maintaining low power by levering these usage differences. It consists of a three-pronged approach: First, reducing active memory footprint size by analyzing the usage patterns of mobile users by developing a usage logging program. Second, developing intelligent error correction schemes that can reduce the refresh energy consumed by the memory system without compromising data integrity. Third, reducing the memory power by utilizing emerging Non Volatile Memory (NVM) technologies and developing data partitioning techniques that can keep critical data in NVM. Memory power consumption continues to be one of the main limiter of the battery life of mobile platforms. In additional to the general use as smartphone and tablets, efficient mobile memory system has use in several other application domains such as surveillance, automotive, environment, military, and biomedical. The techniques developed in our proposal will be useful for these domains as well. The infrastructure on usage logging and the usage characterization will foster other studies in the area of mobile memory systems.