CAREER: Application-specific Power Management

大量现有和新兴的计算应用，包括物联网、传感器网络、可穿戴电子设备和生物医学设备，都具有超低功耗要求。在这些应用所使用的低功耗嵌入式系统中，能效是决定关键系统特性（如尺寸、重量、成本、可靠性和寿命）的主要因素。用于这些系统的现有功率管理技术权衡性能以降低功率。然而，考虑到新兴和现有低功率系统的严格功率和能量约束，牺牲性能以降低功率的解决方案可能是不可接受的。因此，这项研究的重点是在不降低性能的情况下降低功耗的新机会，从而为新兴的低功耗系统提供免费电源和节能，进而降低其尺寸，重量和成本，并提高其可靠性和寿命。这项研究还提供了一种非侵入性的方式，可以显着提高现有系统的能源效率，而无需牺牲任何性能或重新设计系统硬件或软件。考虑到正在生产的低功耗处理器的绝对数量、它们对未来技术的重要性以及这些系统的严格功率和能量限制，在此类系统中节省功率可能会产生重大影响。除了这项研究的技术影响外，该项目还将带来其他几个好处，包括为本科生和代表性不足的群体的学生提供新的基于项目的研究机会，通过公共项目展示，向K-12学生和大学和社区的其他成员进行社区宣传，并以肯尼亚教育倡议的形式进行全球推广，以改善物联网技术农业的最佳实践。描述项目活动对学生学习成果的影响的数据将用于改善PI的研究和教育活动的整合。该项目的研究贡献源于硬件-软件协同分析的新技术的开发，该技术可以识别应用程序可以在处理器中使用的最大硬件资源集，而不管应用程序的输入。这种协同分析的结果可以用于消除应用程序永远无法使用的资源所消耗的任何功率。可以利用这种新颖的协同分析方法来实现一套自动化的应用特定功率管理技术，包括应用特定时序分析，其识别应用可以在处理器中运用的最长路径，并确定保证对应用安全的应用特定的低于标称的最小操作电压;特定于应用的功率域形成和功率门控，与现有技术的功率门控技术相比，其可以提供在更长的时间段内对更大的逻辑区域进行功率门控的机会;特定于应用的峰值功率和能量管理，保证了特定于应用的设计界限？该解决方案可满足客户的峰值功率和能量要求，并支持针对特定应用的能量存储和收集组件调整;以及针对特定应用的热管理，可识别和避免热点，防止热紧急情况，并针对给定应用执行温度感知调度。

A large number of existing and emerging computing applications, including the internet of things, sensor networks, wearable electronics, and biomedical devices, have ultra-low-power requirements. In the low-power embedded systems used by these applications, energy efficiency is the primary factor that determines critical system characteristics such as size, weight, cost, reliability, and lifetime. Existing power management techniques for these systems trade off performance to reduce power. However, given the stringent power and energy constraints of emerging and existing low-power systems, solutions that sacrifice performance to reduce power may be unacceptable. Thus, this research focuses on novel opportunities to reduce power without reducing performance, thereby providing free power and energy savings for emerging low-power systems, and in turn, reducing their size, weight, and cost, and increasing their reliability and lifetime. This research also provides a non-intrusive way to significantly improve the energy efficiency of existing systems without sacrificing any performance or re-designing system hardware or software. Considering the sheer number of low-power processors being produced, their importance for future technologies, and the stringent power and energy constraints of these systems, saving power in such systems can have a significant impact. In addition to the technological impacts of this research, the project will contribute several other benefits, including new project-based research opportunities for undergraduate students and students from underrepresented groups, community outreach to K-12 students and other members of the university and community at large through public project showcases, and global outreach in the form of an educational initiative in Kenya on improving best practices in farming with IoT technology. Data characterizing the impacts of project activities on student learning outcomes will be used to improve the integration of research and educational activities at the PI?s institution.The research contributions of this project stem from the development of novel techniques for hardware-software co-analysis that can identify the maximal set of hardware resources that an application can use in a processor, irrespective of application inputs. The results of such co-analysis can be used to eliminate any power expended by resources that an application can never use. This novel co-analysis approach can be leveraged to enable a suite of automated application-specific power management techniques, including application-specific timing analysis, which identifies the longest paths that an application can exercise in a processor and determines an application-specific lower-than-nominal minimum operating voltage that is guaranteed to be safe for the application; application-specific power domain formation and power gating, which can provide opportunities to power gate larger areas of logic for longer periods of time than state-of-the-art power gating techniques; application-specific peak power and energy management, which guarantees application-specific bounds on a design?s peak power and energy requirements and enables application-specific sizing for energy storage and harvesting components; and application-specific thermal management, which can identify and avoid hotspots, prevent thermal emergencies, and perform temperature-aware scheduling for a given application.