NSF Workshop: Machine Learning Hardware Breakthroughs Towards Green AI and Ubiquitous On-Device Intelligence. To be Held in November 2020.

NSF 研讨会：机器学习硬件突破绿色人工智能和无处不在的设备智能。

• 批准号: 2054865
• 负责人: Yingyan Lin
• 金额: $ 1.51万
• 依托单位: William Marsh Rice University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-12-01 至 2021-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2054865&HistoricalAwards=false
• 关键词: NSF; Workshop; Machine; Learning; Hardware

This workshop aims to bring together experts from academia, industry, and government agencies to discuss and identify visionary research opportunities and challenges for machine learning hardware breakthroughs towards green AI and ubiquitous machine learning powered intelligence. In addition, the workshop will provide opportunities to form collaborative research from different disciplines. This three-day workshop will be held virtually in November 2020. It will feature keynote speeches, panel presentations and discussions, as well as break-out and summary sessions, with the objective of bringing different research communities together, defining important research challenges and promoting machine learning hardware breakthroughs. Intellectual Merit: There has been a critical growing need for innovative machine learning hardware which has the potential to bring orders-of-magnitude hardware efficiency. However, the development of machine learning hardware is much slower than that of machine learning algorithms. This is because developing customized machine learning accelerators presents significant challenges due to (1) the need for cross-disciplinary knowledge in machine learning, micro-architecture, and physical chip design and (2) the large design space resulting from the numerous design choices of dataflows, processing elements, and memory hierarchy. This workshop aims to bring together researchers with a diversified set of expertise to discuss and identify research opportunities and challenges for machine learning hardware (both electrical and optical implementation) to assist in a road map for achieving breakthroughs in artificial intelligence (AI) and ubiquitous machine learning powered intelligence. Broader Impacts: This workshop will bring in researchers with complementary backgrounds to offer different perspectives on potential research challenges and directions for enabling machine learning hardware breakthroughs. Novel ideas could be generated to solve the future challenges for electrical and optical implementation of machine learning hardware. Innovation and commercialization opportunities may be identified following the research ideas. Researchers will have an unparalleled opportunity to build collaborative scholarly and institutional partnerships that transcend boundaries imposed by their respective technical areas. Finally, participation of researchers from underrepresented groups as well as early-career researchers will be encouraged. Results from the workshop will be disseminated through workshop reports.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.

本次研讨会旨在汇聚来自学术界、工业界和政府机构的专家，讨论并确定机器学习硬件突破向绿色AI和无所不在的机器学习推动的智能的富有远见的研究机会和挑战。此外，研讨会将提供机会，形成来自不同学科的合作研究。这一为期三天的研讨会将于2020年11月举行。它将以主旨演讲、小组陈述和讨论以及分组和总结会议为特色，目的是将不同的研究界聚集在一起，确定重要的研究挑战，并促进机器学习硬件的突破。智力优势：对创新的机器学习硬件的需求日益增长，这种硬件有可能带来数量级的硬件效率。然而，机器学习硬件的发展远远慢于机器学习算法的发展。这是因为开发定制的机器学习加速器面临着巨大的挑战，这是因为(1)需要机器学习、微体系结构和物理芯片设计方面的跨学科知识，以及(2)数据流、处理元件和存储器层次结构的大量设计选择导致了巨大的设计空间。本次研讨会旨在将拥有不同专业知识的研究人员聚集在一起，讨论并确定机器学习硬件(电气和光学实施)的研究机会和挑战，以协助制定在人工智能(AI)和无所不在的机器学习支持的智能方面实现突破的路线图。更广泛的影响：本次研讨会将引入具有互补背景的研究人员，就实现机器学习硬件突破的潜在研究挑战和方向提供不同的观点。可以产生新的想法来解决机器学习硬件的电气和光学实现的未来挑战。根据研究思路，可以确定创新和商业化机会。研究人员将有一个无与伦比的机会，建立合作的学术和机构伙伴关系，超越各自技术领域的界限。最后，将鼓励来自代表性不足群体的研究人员以及职业生涯早期研究人员的参与。研讨会的结果将通过研讨会报告传播。这一奖项反映了NSF的法定使命，并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。