FoMR: Improving Microprocessor IPC for Data Center Workloads

越来越多的公司和机构正在将他们的产品和服务转移到云中，显著增加了在数据中心花费的计算周期。这种云计算的爆炸性增长为社会带来了巨大的价值，但代价是增加了我们世界的环境足迹。该项目可以通过设计和优化云应用专用微处理器来提高数据中心系统的能效。这项研究将融入加州大学圣克鲁斯分校的本科课程，教育下一代学生设计节能的数据中心特定架构和系统。该项目将开发预测数据中心应用程序行为的机器学习技术，并将其应用于处理器微架构。特别是，将开发一种新的跨层方法来提高数据中心工作负载优化处理器的每周期指令数(IPC)性能。它将探索和开发一系列挑战的解决方案，包括离线学习数据中心应用程序行为，利用机器学习模型进行指令预取和分支预测，以及构建资源和能源效率高的硬件以在执行时利用机器学习模型。该奖项反映了NSF的法定使命，并已通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

More and more companies and institutions are moving their products and services into the cloud, increasing the number of compute cycles spent in data centers significantly. The explosive growth of such cloud computation has delivered great value to society at the cost of increasing the environmental footprint on our world. This project can improve the energy efficiency of data center systems through the design and optimization of cloud application-specific microprocessors. The research will be infused into the undergraduate curriculum at University of California Santa Cruz, educating the next generation of students on designing energy efficient, data center specific architecture and systems.This project will develop machine learning techniques to predict datacenter application behavior and apply them to processor microarchitecture. In particular, a new cross-layer approach will be developed to improve the instructions-per-cycle (IPC) performance of data center workload-optimized processors. It will explore and develop solutions for a number of challenges including offline learning of data center application behavior, leveraging machine learning models for instruction prefetching and branch prediction, and building resource and power efficient hardware to leverage the machine learning models at execution time.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.