CAREER: Energy-Efficient Parallel Architectures for Computer Vision

在生物有机体的进化过程中，数百万年来，视觉能力一直是一个普遍的特征。然而，今天制造的大多数机器基本上对不断轰击它们的光子视而不见。有效的实时计算机视觉有无数的应用，并将对经济和安全产生变革性的影响。让机器能够看到东西不仅依赖于计算机视觉研究的进步，还依赖于硬件平台的设计，这些硬件平台将以足够的能量效率执行这些算法。直到最近，传统观点还认为，制造晶体管数量呈指数级增长的芯片的能力，将为我们带来足够快的处理器，无论我们想要运行哪种算法。不幸的是，功率限制限制了芯片在任何时候可以使用的晶体管数量。因此，能量效率是影响未来视觉算法是否能解决实时视觉问题的关键指标。这项研究的重点是围绕Stingray的设计，这是一种具有许多大规模专业，各种处理内核的芯片，用于视觉处理应用的最大能源效率。该项目探讨了在设计有效的低功耗Stingray系统时出现的这些和其他建筑挑战。它使学生接触到广泛的问题，包括并行编译、视觉和硬件设计。它试图捕捉视觉应用程序的属性，以构建一个完整的原型视觉处理器。该奖项是根据2009年美国复苏和再投资法案（公法111-5）资助的。

In the evolution of biological organisms, the ability to see has been a popular feature for millions of years. However, the majority of machines built today remain essentially blind to the photons that continually bombard them. Effective real-time computer vision has countless applications and would have a transformative effect on the economy and safety. Enabling machines to see relies not only on the advancement of computer vision research, but also the design of the hardware platforms that will execute these algorithms with sufficient energy efficiency.Until recently, the conventional wisdom was that the ability to manufacture chips with exponentially increasing numbers of transistors will bring us processors that are fast enough for whichever algorithm it is we want to run. Unfortunately, power limitations are limiting how many of these transistors a chip can use at any one time. As a result, energy efficiency is the critical metric which will influence whether future vision algorithms are viable solutions to the real-time vision problems. This research focuses around the design of Stingray, a chip with many massively specialized, diverse kinds of processing cores, which is tuned for maximal energy efficiency in vision processing applications. The project explores these and other architectural challenges that arise in designing effective low power Stingray systems. It exposes students to a broad range of issues including parallel compilation, vision, and hardware design. It seeks to capture the properties of vision applications to build a complete, prototype vision processor.This award is funded under the American Recovery and Reinvestment Act of 2009(Public Law 111-5).