Conference: NSF Workshop on Hardware-Software Co-design for Neuro-Symbolic Computation

会议：NSF 神经符号计算软硬件协同设计研讨会

• 批准号: 2338640
• 负责人: Hai Li
• 金额: $ 4.98万
• 依托单位: Duke University
• 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
• 起止时间: 2023-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2338640&HistoricalAwards=false
• 关键词: Conference; NSF; Workshop; Hardware; Software

Despite the impressive advancements in artificial intelligence (AI) systems, there still exist significant challenges, such as enabling high-level reasoning in real-world contexts and dynamic compositionality within resource-constrained environments. Neuro-symbolic computation represents the convergence of two prominent AI methodologies: traditional symbolic and neural networks. By integrating symbolic and statistical methods, neuro-symbolic computation unleashes essential features of modularity for explainability and compositionality. However, the approach introduces significant computational complexity. The purpose of this project is to organize a National Science Foundation (NSF) sponsored workshop named "Hardware-Software Co-design for Neuro-Symbolic Computation."The primary objective of this workshop is to gather leading experts in relevant research domains to explore and identify the scientific and technological challenges associated with high-performance, integrated neuro-symbolic AI computing systems, and provide valuable guidance to the broader research community. To ensure comprehensive coverage of the topic, the team will extend invitations to prominent researchers from academia, industry, government laboratories, and funding agencies with diverse backgrounds, including circuit design, computer architecture, design automation, system development, algorithms, and applications. Underrepresented groups will be particularly encouraged to attend the workshop and receive priority in the considerations. This project aims to identify the critical strategies for semiconductor and AI workforce development and technology transfer.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.

尽管人工智能（AI）系统取得了令人印象深刻的进步，但仍然存在重大挑战，例如在现实世界中的上下文中实现高级推理以及在资源受限环境中的动态组成性。神经符号计算代表了两种突出的AI方法的收敛：传统的符号和神经网络。通过整合符号和统计方法，神经符号计算释放了模块化的基本特征，可解释性和组成性。但是，该方法引入了重大的计算复杂性。 The purpose of this project is to organize a National Science Foundation (NSF) sponsored workshop named "Hardware-Software Co-design for Neuro-Symbolic Computation."The primary objective of this workshop is to gather leading experts in relevant research domains to explore and identify the scientific and technological challenges associated with high-performance, integrated neuro-symbolic AI computing systems, and provide valuable guidance to the broader research community.为了确保对该主题的全面报道，该团队将向来自学术界，行业，政府实验室以及具有不同背景的资助机构的著名研究人员扩展邀请，包括电路设计，计算机建筑，设计自动化，系统开发，系统开发，算法和应用。特别鼓励代表性不足的团体参加研讨会并在考虑方面获得优先级。该项目旨在确定半导体和AI劳动力发展和技术转移的关键策略。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响评估标准通过评估来支持的。