课题基金 / 基金详情

Travel: Workshop on Shared Infrastructure for Machine Learning Electronic Design Automation

Travel: Workshop on Shared Infrastructure for Machine Learning Electronic Design Automation
旅行:机器学习电子设计自动化共享基础设施研讨会
批准号:
2310319
负责人:
Jiang Hu
金额:
$1.0万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2023
资助国家:
美国
项目状态:
已结题
起止时间:
2023-03-01 至 2024-02-29

项目摘要

项目成果

Jiang Hu的其他基金

相似基金

相关文献

中文摘要
翻译
该奖项将涵盖电子设计自动化(EDA)机器学习(ML)工具开发共享基础设施研讨会的差旅费。虽然机器学习技术已经在EDA应用中证明了它们的有效性,并在工业中被采用,但这种发展面临着巨大的数据准备成本的瓶颈。此外,私人基础设施阻碍了相关研究成果的可比性和再现性。该研讨会将汇集相关学生,学术研究人员,工业专家和政府官员,讨论电子设计自动化机器学习共享基础设施的需求,挑战和解决方案,并概述未来行动项目的路线图。研讨会的参与者将包括来自不同人群的与会者,包括妇女和代表性不足的群体。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,被认为值得支持。
英文摘要
This award will cover the travel expenses for a workshop on shared infrastructure for development of Machine Learning (ML) tools for Electronic Design Automation (EDA). Although machine learning techniques have demonstrated their effectiveness in EDA applications and been adopted in industry, such developments face the bottleneck of tremendous data preparation cost. In addition, private infrastructures hinder the comparability and reproducibility of related results of research. This workshop will bring together related students, academic researchers, industrial experts and government officials to discuss the needs, challenges and solutions regarding shared infrastructure for Machine Learning for Electronic Design Automation and outline a roadmap for future action items. The workshop participants will include attendees from a diversity of population including women, and members of underrepresented groups. The workshop summary report will be disseminated for public consumption.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.
期刊论文(0)
专著(0)
科研奖励(0)
会议论文
Collaborative Research: SHF: Medium: Automated energy-efficient sensor data winnowing using native analog processing
Collaborative Research: SHF: Medium: Revitalizing EDA from a Machine Learning Perspective
RTML: Small: Real-Time Model-Based Bayesian Reinforcement Learning
STARSS: Small: Collaborative: Physical Design for Secure Split Manufacturing of ICs
海外基金