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

• 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

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源收集能量，例如太阳能、热能和射频辐射。由于不可靠的能源，能量收集系统遭受频繁的电力故障。因此，能量收集系统应该能够在电源故障之前保存当前程序状态，在电源恢复时恢复一致的程序状态，并且无缝地恢复程序执行，就像什么都没有发生一样。然而，在电源周期中保持崩溃一致性状态是具有挑战性的。因此，当前一代的能量收集系统设计有简单的硬件配置，例如没有高速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，允许能量收集系统利用缓存和图形处理单元（GPU）实现高性能和高能效。该项目预计将作为解锁下一代物联网服务的基础，基于无电池能量收集系统。该项目还旨在将研究成果纳入本科教学，并为女性学生提供K-12推广计划，以促进女性在计算机科学领域获得更公平的成果。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高能效的下一代能量收集系统。为此，该项目提出了基于编译器和硬件的解决方案，在三个研究方向。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，避免在运行时进行昂贵的日志记录，而是在重启时恢复潜在的非持久化存储。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，它结合了回写缓存和直写缓存的优点，而没有各自的缺点。该项目将设计第一个能量收集GPU系统，为GPU寄存器引入新的检查点解决方案，并为GPU共享内存引入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价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

DevFuzz: Automatic Device Model-Guided Device Driver Fuzzing

• DOI: 10.1109/sp46215.2023.10179293
• 发表时间: 2023-05
• 期刊: 2023 IEEE Symposium on Security and Privacy (SP)
• 作者: Yilun Wu;Tong Zhang;Changhee Jung;Dongyoon Lee

Write-Light Cache for Energy Harvesting Systems

• DOI: 10.1145/3579371.3589098
• 发表时间: 2023-06
• 期刊: Proceedings of the 50th Annual International Symposium on Computer Architecture
• 作者: Jongouk Choi;Jianping Zeng;Dongyoon Lee;Changwoo Min;Changhee Jung

DURINN: Adversarial Memory and Thread Interleaving for Detecting Durable Linearizability Bugs

• 发表时间: 2022
• 作者: Xinwei Fu;Dongyoon Lee;Changwoo Min