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

William Marsh Rice University, Houston TX

Investigators

Abstract

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.