CyberTraining: Pilot: Linear Algebra Preparation for Emergent Neural Network Architectures (LAPENNA)

网络培训：试点：紧急神经网络架构的线性代数准备 (LAPENNA)

• 批准号: 2017673
• 负责人: Kwai Wong
• 金额: $ 29.98万
• 依托单位: University of Tennessee Knoxville
• 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: 2020-07-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2017673&HistoricalAwards=false
• 关键词: CyberTraining; Pilot; Linear; Algebra; Preparation

In this project, the Linear Algebra Preparation for Emergent Neural Network Architectures (LAPENNA) program is organized to provide essential knowledge to advance literacy in AI to sustain the growth and development of the workforce in the cyberinfrastructure (CI) ecosystem of data-driven science. This program provides integrated expertise to faculty, students, and researchers the knowledge in numerical mathematics, linear algebra software, data-driven methods, and machine learning tools to tackle day to day problems in data science applications. This program aims to prepare college researchers to enable, design, and direct their own in-house data-driven science programs and incorporate perspectives from their research into their course curricula. The knowledge and experiences gathered under the direction of LAPENNA will be beneficial to CI practitioners as well as to general interested parties leading to fostering new collaborative partners and potential research initiatives and workforce training programs.LAPENNA focuses on delivering algorithmic and computational techniques, numerical and programming procedures, and AI software implementation on emergent CPU cloud systems and GPU platforms. It runs two training sessions every year. Ten webinars/lectures are delivered with supporting online tutorials available for general public use. In each session, eight teams of faculty/researchers and students are selected to participate in the LEPENNA program. Each team consists of two members from an institution. The training for each cohort lasts for six months and concludes with an on-site one-week workshop. Follow-up Q & A sessions connect the college teams and PIs during and after the training events and continue to provide hardware and software support to them. LAPENNA delivers online materials that are useful and available to general CI practitioners.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.

在该项目中，组织了用于紧急神经网络架构的线性代数准备（LAPENNA）计划，以提供必要的知识来提高人工智能的素养，以维持数据驱动科学的网络基础设施（CI）生态系统中劳动力的增长和发展。该计划为教师，学生和研究人员提供综合专业知识，包括数值数学，线性代数软件，数据驱动方法和机器学习工具，以解决数据科学应用中的日常问题。该项目旨在帮助大学研究人员实现、设计和指导他们自己的内部数据驱动科学项目，并将他们的研究观点纳入他们的课程课程。在LAPENNA的指导下收集的知识和经验将有利于CI从业者以及一般感兴趣的各方，从而培养新的合作伙伴和潜在的研究计划和劳动力培训计划。LAPENNA专注于在新兴的CPU云系统和GPU平台上提供算法和计算技术、数值和编程程序以及人工智能软件实现。它每年举办两次培训。十场网络研讨会/讲座提供了支持的在线教程，供公众使用。在每一届会议中，有八组教师/研究人员和学生被选中参加LEPENNA项目。每队由来自一个机构的两名成员组成。每个队列的培训持续六个月，最后是一个为期一周的现场研讨会。在培训活动期间和之后，后续的问答环节将学院团队和pi联系起来，并继续为他们提供硬件和软件支持。LAPENNA提供对一般CI从业者有用和可用的在线材料。该奖项反映了美国国家科学基金会的法定使命，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。