EAGER: Statistical Learning in Chip

综合电子系统在我们生活的方方面面无处不在，从电脑到手机再到汽车，它的应用无处不在。然而，由于底层技术固有的巨大复杂性，设计和制造既能工作又可靠的系统变得极其困难。在这个NSF EAGER项目中，我们将展示芯片中的统计学习（SLIC）如何应对由于不精确的设计和制造而产生的非理想性，以及源于系统用户和操作环境的不确定性。具体来说，SLIC将使集成系统能够在各种应用中“学习”最佳工作点，从而最大化性能并最小化功耗。这将通过为芯片内实现开发定制的统计学习算法来实现，该算法能够从在线和离线产生的系统数据中获得可操作的信息。首席研究员（PI）致力于通过培养多样化的本科生和研究生研究人员来产生更广泛的影响。他的研究小组成员来自代表性不足的群体，包括女性、非洲裔美国人、西班牙裔美国人和印第安人。此外，作为卡内基梅隆大学（Carnegie Mellon University）硅系统实现中心（CSSI）的主任，PI管理着一个项目，该项目从各个大学（包括少数族裔服务机构）招募本科生研究人员，并负责全国黑人工程师协会的年度会议。这个项目非常成功，招募了许多本科生研究人员，包括女性和非裔美国人。在过去的几年里，PI已经指导了9名本科生研究人员，其中3名是非裔美国人（2男1女），并将继续招收研究生和本科生等不同群体的学生参与该项目。

Integrated electronic systems are pervasive in all aspects of our lives, used in everything from computers to mobile phones to automobiles. However, designing and manufacturing systems that both work and are reliable is becoming extremely difficult due to the significant complexity inherent in the underlying technology. In this NSF EAGER project, we will demonstrate how statistical learning in chip (SLIC) can cope with the non-idealities that arise due to imprecise design and fabrication, and the uncertainty that stems from the system's user and operating environment. Specifically, SLIC will enable an integrated system to "learn" optimal operating points across various applications so as to maximize performance, and to minimize power consumption. This will be accomplished by developing customized statistical learning algorithms for in-chip implementation that are capable of deriving actionable information from system data produced both on- and off-line. The principal investigator (PI) is committed to having a broader impact through training a diverse group of undergraduate and graduate researchers. His research group has members from under-represented groups that include women, African Americans, Hispanic Americans, and Native Americans. In addition, as director of the Center for the Silicon System Implementation (CSSI) at Carnegie Mellon University, the PI manages a program that recruits undergrads researchers from various universities (including minority-serving institutions), and the annual convention of the National Society of Black Engineers. This program has been very successful, resulting in the recruitment of many undergraduate researchers, including both women and African-Americans. In the last few years, the PI has supervised nine undergraduate researchers, three of which were African-American (two male and one female), and will continue to recruit a diverse group of students, both at the graduate and undergraduate levels, for participation in this project.