SHF: Small: Collaborative Research:Variation-Resilient VLSI Systems with Cross-Layer Controlled Approximation

SHF：小型：协作研究：具有跨层控制逼近的抗变化 VLSI 系统

• 批准号: 1525925
• 负责人: Sachin Sapatnekar
• 金额: $ 21万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-01 至 2019-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1525925&HistoricalAwards=false
• 关键词: SHF; Small; Collaborative; Research; Variation

Applications driven by human-computer interactions through the five human senses are projected to underpin the next generation of computing. For many of these applications, occasional small errors are often not only acceptable but also bring opportunities for building lighter, cheaper, and more robust systems that use less energy and may have a longer battery life. This project will study how to advance computing technology by allowing deliberate imprecision in hardware implementations through the notion of approximate computing. The outcomes of this project will be a set of design techniques for approximate computing that can become a key component of hardware computing technology, potentially benefiting systems ranging from high performance computing for big data analytics and low power implementation for internet of things. This project will also provide an opportunity for training students with the latest design and computing technology. The research goals of this project are to create new approximate computing techniques to optimize a system at all stages of its life, from design-time to runtime, which can enable cross-layer control of performance-power-precision trade-offs. The research agenda consists of several components. First, new error models with different accuracy-complexity trade-offs will be developed. Second, new design-time optimization techniques, especially hardware resource scheduling and binding in high-level synthesis, will be studied with consideration of approximation, variation, and runtime circuit reconfiguration. Third, compile-time and operating-system-level task mapping/scheduling algorithms will be investigated to make the best use of circuits with various precisions. Last but not least, runtime precision control techniques will be explored in conjunction with dynamic voltage and frequency scaling in order to achieve a smooth trade-off between power and user experience.

通过人类五种感官的人机交互驱动的应用程序预计将支撑下一代计算。对于许多此类应用来说，偶尔的小错误通常不仅是可以接受的，而且还为构建更轻、更便宜、更强大的系统带来了机会，这些系统使用更少的能源，并且可能具有更长的电池寿命。该项目将研究如何通过近似计算的概念允许硬件实现中故意不精确，从而推进计算技术。 该项目的成果将是一套近似计算的设计技术，可以成为硬件计算技术的关键组成部分，可能使大数据分析的高性能计算和物联网的低功耗实现等系统受益。该项目还将为学生提供最新设计和计算技术的培训机会。该项目的研究目标是创建新的近似计算技术来优化系统生命周期的各个阶段（从设计时到运行时），从而实现性能-功耗-精度权衡的跨层控制。研究议程由几个部分组成。首先，将开发具有不同精度-复杂性权衡的新误差模型。其次，将研究新的设计时优化技术，特别是高级综合中的硬件资源调度和绑定，同时考虑近似、变化和运行时电路重新配置。第三，将研究编译时和操作系统级任务映射/调度算法，以充分利用各种精度的电路。最后但并非最不重要的一点是，将结合动态电压和频率缩放来探索运行时精度控制技术，以实现功耗和用户体验之间的平滑权衡。