CCF Core: Small: Hardware/Software Co-Design for Sustainability at the Edge

由于云服务器和边缘服务器的广泛普及，开发用于边缘和云计算以及人工智能（AI）加速的可持续加速方法对整个社会特别是计算机科学界都是有益的。减少碳和甲烷排放对于实现2030年和2050年减少气候变暖的环境目标非常重要。该项目解决了边缘计算和云计算日益普及对执行人工智能的更广泛影响，以建立超越纯粹能源效率的可持续计算系统设计方法。与专注于降低能耗和碳排放的节能设计不同，该项目将建立一个硬件/软件协同设计框架，以生成满足功能、性能、可持续性和其他系统要求的特定约束的最佳硬件和算法设计。该框架将指导未来的可持续硬件设计。为了实现这一目标，提出的框架在四个层面上进行整体协同设计——建模、算法、调度和硬件。通过引入计算范式和新兴人工智能应用之间的新协同作用，本研究的成果将使整个人工智能行业受益，从硬件开发到算法设计以及最终用户的应用。提议的协同设计框架将是社区中第一个建立整体可持续的人工智能系统的研究，该系统最近被认为是新兴的重要性。该项目的成功将为未来的可持续计算系统设计铺平道路。教育工作旨在培养学生对可持续计算、当代计算机架构和人工智能研究的兴趣。现有的计算机组织、算法和计算系统课程将通过可持续计算、边缘计算和机器学习等跨学科研究课题以及构建模拟原型的实践经验得到加强。将特别注意招募代表性不足的群体，并通过新的教育论坛丰富studentsí的学习经验。该项目由分配给可持续计算设计的基金（NSF 22-060）资助。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，认为值得支持。

Due to the wide proliferation of cloud and now edge servers, the development of sustainable acceleration methods for edge and cloud computing and artificial intelligence (AI) acceleration is beneficial to society broadly and the computer science community, in particular. Reducing carbon and methane emissions is important to reaching environmental goals for reducing warming for which targets of 2030 and 2050 have been set. This project tackles the broader implications of the growing popularity of edge and cloud computing for executing AI to establish a sustainable computing system design methodology beyond pure energy efficiency. Unlike solely energy-efficient designs that focus on reducing power consumption and carbon emissions as a by-product, this project will build a hardware/software co-design framework to generate the optimal hardware and algorithm designs that meet specific constraints of functionality, performance, sustainability, and other system requirements. The framework will guide future sustainable hardware design. Towards this goal, the proposed framework engages holistic co-design efforts across four levels - modeling, algorithm, scheduling, and hardware.By introducing new synergies between computing paradigms and emerging AI applications, the outcomes of this research will benefit the entire AI industry, from hardware development to algorithm design and the applications for the end-users. The proposed co-design framework will be the first research in the community to build holistically sustainable AI systems which are recently recognized as emerging in importance. The success of this project will pave the road for future sustainable computing system design. The educational efforts aim at cultivating students' interests in the study of sustainable computing, contemporary computer architecture, and artificial intelligence. The existing curricula of computer organization, algorithms, and computing systems will be enhanced by the interdisciplinary research topics on sustainable computing, edge computing, and machine learning, as well as the hands-on experiences in building the simulation prototypes. Special attention will be given to recruiting underrepresented groups and enriching studentsí study experiences through new education forums.This project is funded by funds allocated to Design for Sustainable Computing (NSF 22-060)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.