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、可穿戴电子产品和生物医学设备，都有超低功耗要求。在这些应用使用的低功耗嵌入式系统中，能源效率是决定关键系统特性（如尺寸、重量、成本、可靠性和使用寿命）的主要因素。这些系统的现有电源管理技术通过牺牲性能来降低功耗。然而，考虑到新兴和现有低功耗系统的严格功率和能量限制，牺牲性能以降低功耗的解决方案可能是不可接受的。因此，本研究的重点是寻找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的功耗和节能，从而减小其尺寸、重量和成本，并提高其可靠性和使用寿命。本研究还提供了一种非侵入式的方法，可以在不牺牲任何性能或重新设计系统硬件或软件的情况下显著提高现有系统的能源效率。考虑到正在生产的低功耗处理器的数量，它们对未来技术的重要性，以及这些系统严格的功率和能量限制，在这些系统中节省功率可以产生重大影响。除了这项研究的技术影响外，该项目还将带来其他一些好处，包括为本科生和代表性不足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学生和大学和整个社区的其他成员进行社区推广，以及在肯尼亚开展一项教育计划，以提高物联网技术在农业方面的最佳实践。描述项目活动对学生学习成果影响的数据将用于改善PI的研究和教育活动的整合。年代的机构。该项目的研究贡献源于硬件-软件协同分析的新技术的发展，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，而不考虑应用程序的输入。这种共同分析的结果可用于消除应用程序永远不会使用的资源所消耗的任何功率。这种新颖的协同分析方法可以用来实现一套自动化的特定应用的电源管理技术，包括特定应用的时序分析，它可以识别应用可以在处理器中运行的最长路径，并确定特定应用的低于标称的最小工作电压，以保证应用的安全；特定应用的功率域形成和功率门控，与最先进的功率门控技术相比，可以提供更大范围的逻辑功率门控，时间更长；特定于应用程序的峰值功率和能量管理，它保证了特定于应用程序的设计界限？S峰值功率和能量需求，并使能量存储和收集组件的应用特定尺寸；针对特定应用程序的热管理，可以识别和避免热点，防止热紧急情况，并为给定应用程序执行温度感知调度。

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.