SHF:CSR:Small:Improving Processor Efficiency with Prediction

SHF:CSR:Small：通过预测提高处理器效率

• 批准号: 1332598
• 负责人: Daniel Jimenez
• 金额: $ 35万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-01-01 至 2017-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1332598&HistoricalAwards=false
• 关键词: SHF; CSR; Small; Improving; Processor

For several decades, computers have been getting faster. Much of this improvement in performance is due to Moore's Law, i.e., the rapid growth in the number of devices that can be integrated into a single microchip. However, techniques that exploit the growing resources provided by Moore's Law are often inefficient and wasteful. This project takes a hard look at techniques used to improve performance, showing how resource-hungry techniques can be made less wasteful. The improved structures use techniques borrowed from other areas of Computer Science to predict the near-term resource usage of the computer to do a better job of allocating those resources. These prediction techniques result in a system that is both more power efficient and better performing.Improvements in power efficiency and performance have a wide-ranging impact, from improving battery life in mobile devices to reducing energy costs and environmental impact of data centers. The project will involve university students in research, helping to train the next generation of technology workers and educators.This project applies microarchitectural prediction techniques to recover wasted resources and thus improve the efficiency of microprocessors. The project explores the following opportunities for reducing waste with prediction: 1) applying highly accurate branch prediction techniques to other domains such as caches, 2) using mixed analog/digital circuit implementations to improve prediction accuracy while reducing waste in the predictor itself, and 3) developing a set of new confidence estimation techniques and considering their use in a variety of microarchitectural optimizations.

几十年来，计算机一直在越来越快。 性能的大部分改进是由于摩尔定律，即，可以集成到单个微芯片中的设备数量的迅速增长。 但是，利用摩尔法律提供的不断增长的资源的技术通常效率低下和浪费。 该项目仔细研究了用于提高性能的技术，并表明了如何使渴望资源的技术降低浪费。 改进的结构使用从其他计算机科学领域借来的技术来预测计算机的近期资源使用，以更好地分配这些资源。 这些预测技术会导致一个既具有功率效率又更好的性能的系统。功率效率和性能的改善会产生广泛的影响，从改善移动设备的电池寿命到降低数据中心的能源成本和环境影响。该项目将使大学生参与研究，帮助培训下一代技术工作者和教育工作者。该项目应用微体系预测技术来收回浪费的资源，从而提高微处理器的效率。 该项目探讨了以下预测减少浪费的机会：1）将高度准确的分支预测技术应用于缓存等其他领域，2）使用混合的模拟/数字电路实施方法来提高预测准确性，同时减少预测器中的浪费，3）3）开发新的置信度估计技术并考虑其在各种微观结构中的使用中的使用。