Collaborative Research: SHF: Small: Enabling Caches and GPUs for Energy Harvesting Systems

合作研究：SHF：小型：为能量收集系统启用缓存和 GPU

基本信息

批准号：
2153747
负责人：
Dongyoon Lee
金额：
$ 20万
依托单位：
SUNY at Stony Brook
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-06-15 至 2025-05-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2153747&HistoricalAwards=false
关键词：
Collaborative Research SHF Small Enabling

项目摘要

Energy harvesting systems collect energy from variant ambient sources such as solar power, thermal energy, and radio-frequency radiation. Due to unreliable energy sources, energy harvesting systems suffer from frequent power failures. Hence, energy harvesting systems should be able to save the current program states before a power failure, restore the consistent program states when the power comes back, and seamlessly resume program execution as if nothing happened. However, maintaining crash-consistent states across power cycles is challenging. As a result, the current generation of energy harvesting systems has been designed with simple hardware configurations such as a single central processing unit (CPU) without a cache, delivering limited computing capabilities. Going forward, in the new Internet of Things era, ever-increasing demand for substantially more high-performance energy-harvesting systems capable of supporting emerging artificial-intelligence and machine-learning applications are expected. This project proposes new software and hardware co-design solutions that allow energy-harvesting systems to leverage caches and graphic processing units (GPUs) for high performance and energy efficiency. The project is expected to serve as the foundation to unlock next-generation Internet of Things services, based on battery-less energy-harvesting systems. The project also aims to incorporate research findings in undergraduate teaching and offer K-12 outreach programs for female students to promote more equitable outcomes for women in computer science.The objective of this project is to enable caches and GPUs in energy-harvesting systems and to design next-generation energy harvesting systems with high performance and energy efficiency. To this end, the project proposes compiler- and hardware-based solutions in three research thrusts. The project will explore a compiler-based solution that allows existing energy-harvesting systems to use a traditional data cache without hardware modification. The project will explore a new research direction that avoids expensive logging at run time, yet instead recovers potentially un-persisted stores at reboot time. To achieve better performance, the project aims to design a new hardware-based cache for energy-harvesting systems, which combines the benefits of a write-back cache and a write-through cache without their respective downsides. The project will design the first energy-harvesting GPU system that introduces a new checkpointing solution for GPU registers and a lightweight persistence solution for GPU shared memory.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.

能量收集系统从各种环境来源收集能量，例如太阳能、热能和射频辐射。由于能源不可靠，能量收集系统经常出现断电的情况。因此，能量收集系统应该能够在断电前保存当前的程序状态，在电源恢复时恢复一致的程序状态，并无缝地恢复程序执行，就像什么都没发生一样。然而，在整个电源周期内保持崩溃一致的状态具有挑战性。因此，当前一代的能量收集系统被设计为具有简单的硬件配置，例如没有缓存的单个中央处理单元（CPU），提供有限的计算能力。展望未来，在新的物联网时代，预计对能够支持新兴人工智能和机器学习应用的高性能能量收集系统的需求将不断增加。该项目提出了新的软件和硬件协同设计解决方案，使能量收集系统能够利用缓存和图形处理单元 (GPU) 来实现高性能和高能效。该项目预计将成为基于无电池能量收集系统的下一代物联网服务的基础。该项目还旨在将研究成果纳入本科教学，并为女学生提供 K-12 推广计划，以促进女性在计算机科学领域取得更公平的成果。该项目的目标是在能量收集系统中启用缓存和 GPU，并设计具有高性能和能源效率的下一代能量收集系统。为此，该项目在三个研究重点中提出了基于编译器和硬件的解决方案。该项目将探索一种基于编译器的解决方案，允许现有的能量收集系统使用传统的数据缓存，而无需修改硬件。该项目将探索一个新的研究方向，避免运行时昂贵的日志记录，而是在重新启动时恢复可能未持久化的存储。为了实现更好的性能，该项目旨在为能量收集系统设计一种新的基于硬件的缓存，它结合了回写式缓存和直写式缓存的优点，而没有各自的缺点。该项目将设计第一个能量收集 GPU 系统，该系统引入了新的 GPU 寄存器检查点解决方案和 GPU 共享内存的轻量级持久性解决方案。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优势进行评估，被认为值得支持以及更广泛的影响审查标准。