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

在过去的十年里，计算平台发生了范式转变。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。能耗是影响移动的手持设备开发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。诸如智能电话之类的设备的使用模式与诸如膝上型计算机和台式计算机之类的传统计算设备非常不同，并且用户对这些手持移动的设备具有不同的期望集合。例如，期望移动的设备至少在一天内始终开启，并且快速启动和响应。另一方面，笔记本电脑/台式机设备预计在大量使用下表现良好，快速启动和响应的期望并不那么严格。此外，在应用程序使用方面，移动的系统倾向于具有低水平的多任务的突发使用，而桌面使用涉及具有高水平的多任务的更连续的长时间运行的程序。 该项目旨在优化移动的设备中的内存系统，以实现快速响应时间，同时通过勒韦林这些使用差异来保持低功耗。 它包括一个三管齐下的方法：第一，通过分析移动的用户的使用模式，减少活动内存占用的大小，通过开发一个使用日志程序。第二，开发智能错误校正方案，其可以减少存储器系统消耗的刷新能量而不损害数据完整性。 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持在NVM中的数据分区技术来降低存储器功率。 存储器功耗仍然是移动的平台的电池寿命的主要限制因素之一。除了作为智能电话和平板电脑的一般用途之外，高效的移动的存储器系统还用于若干其他应用领域，诸如监视、汽车、环境、军事和生物医学。在我们的建议中开发的技术也将对这些领域有用。使用日志和使用特性的基础设施将促进其他研究领域的移动的内存系统。

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.