CAREER: Application-specific Power Management

大量现有的和新兴的计算应用，包括物联网、传感器网络、可穿戴电子设备和生物医学设备，都有超低功耗的要求。在这些应用所使用的低功耗嵌入式系统中，能效是决定系统尺寸、重量、成本、可靠性和寿命等关键系统特性的首要因素。这些系统的现有电源管理技术会牺牲性能以降低功耗。然而，考虑到新兴和现有低功率系统的严格功率和能源限制，牺牲性能以降低功率的解决方案可能是不可接受的。因此，这项研究着眼于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电力和节能，进而降低它们的尺寸、重量和成本，并提高它们的可靠性和寿命。这项研究还提供了一种非侵入性的方法，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效。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，以及这些系统严格的功率和能源限制，在此类系统中节省电能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，包括为本科生和来自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学生以及大学和社区的其他成员进行社区推广，以及在肯尼亚以教育倡议的形式开展全球推广，以改进物联网技术耕作的最佳做法。表征项目活动对学生学习结果影响的数据将被用来改善皮莱S研究所的研究和教育活动的整合。该项目的研究贡献来自于硬件-软件协同分析新技术的开发，该技术可以确定应用程序在处理器中可以使用的最大硬件资源集，而不考虑应用程序的输入。这种联合分析的结果可以用来消除应用程序永远不能使用的资源所消耗的任何电力。这种新颖的协同分析方法可被用来实现一套自动化的特定于应用的功率管理技术，包括特定于应用的时序分析，其识别应用可在处理器中执行的最长路径，并确定保证对应用安全的特定于应用的低于标称的最小工作电压；特定于应用的电源域形成和功率门控，其可以提供在比最先进的功率门控技术更长的时间段内对更大的逻辑区域进行电源选通的机会；特定于应用的峰值功率和能量管理，确保设计的特定应用范围？S峰值功率和能量要求，并实现特定于应用的能量存储和收集组件的大小调整；以及特定于应用的热管理，该管理可以识别和避免热点，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.