Category II: Exploring Neural Network Processors for AI in Science and Engineering

第二类：探索科学与工程中人工智能的神经网络处理器

• 批准号: 2005369
• 负责人: Amitava Majumdar
• 金额: $ 500万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-01 至 2026-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2005369&HistoricalAwards=false
• 关键词: Category; II; Exploring; Neural; Network

Artificial Intelligence (AI) is increasingly applied to science and engineering problems, and as a part of a growing data science field that is enabling new insights and discoveries not possible with traditional high-performance computing architectures. In 2019 the President issued an executive order announcing a national AI strategy involving the private sector, academia, and the public. Among other things, this strategy calls for investments in AI research and development, providing AI resources, and training an AI-ready workforce. Researchers are increasingly applying machine learning (ML) techniques to science and engineering problems including those from astronomy, climate modeling, extreme-scale systems management, cell biology, high energy physics, drug discovery, social science, satellite image analysis, among others. In addition to advances in algorithms and software, the performance of AI systems is heavily dependent on the underlying hardware. Evaluation of hardware optimized for AI algorithms is of keen interest to the AI research community.To extend the application of AI to evermore challenging problems in science and engineering, the San Diego Supercomputer Center (SDSC), working closely with their vendor partner Supermicro, will deploy Voyager, a high-performance, innovative resource for conducting AI research across a wide range of science and engineering domains. Based on AI processors optimized for deep learning (DL) operations, Voyager will be a first-of-its-kind system available in the NSF resource portfolio. This will give researchers the opportunity to explore Voyager’s unique hardware and software using well-established deep learning frameworks like PyTorch, Keras, and Tensorflow to implement deep learning techniques such as convolutional neural networks (CNNs) and generative adversarial networks (GANs). Researchers will also be able to develop their own AI techniques using software tools and libraries built specifically for Voyager’s innovative AI architecture.The project is structured as a three-year Testbed phase followed by a two-year Allocations Phase. During the Testbed phase SDSC researchers and collaborators will work closely with a small number of research teams to evaluate the performance of Voyager’s innovative deep learning (DL) hardware, specialized compilers, and system libraries. Semiannual workshops will bring teams together to share lessons learned, and develop the knowledge and best practices that inform future users who will be given access during the Allocations Phase. During the Allocations Phase, Voyager will be available to researchers with projects deemed meritorious by an NSF-approved allocation process. Lessons learned from the Testbed Phase is used to develop documentation, best practices, allocations models, and user support strategies. Semiannual workshops continue in the Allocations Phase. Through SDSC’s AI Technology Lab, the project will engage with industry to explore how technologies like those in Voyager can improve the global competitiveness of private sector companies, and prepare the next generation workforce.SDSC will deploy Voyager in SDSC's energy-efficient data center on the UCSD campus. Voyager will be connected to multiple high-performance research and education networks at 100 Gbps. Supporting Voyager is a nationally recognized team of application and systems experts at SDSC. The Voyager External Advisory Board will assist in recruiting early users and providing guidance to the project.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.

人工智能 (AI) 越来越多地应用于科学和工程问题，并且作为不断发展的数据科学领域的一部分，它正在实现传统高性能计算架构无法实现的新见解和发现。2019 年，总统发布了一项行政命令公告。国家人工智能战略要求私营部门、学术界和公众投资人工智能研究和开发，提供人工智能资源，并培训研究人员越来越多地应用机器学习（ML）。科学与工程技术问题包括天文学、气候建模、极端规模系统管理、细胞生物学、高能物理、药物发现、社会科学、卫星图像分析等。除了算法和软件的进步之外，人工智能系统的性能也在不断提高。严重依赖于底层硬件的人工智能算法优化硬件的评估引起了人工智能研究界的浓厚兴趣。为了将人工智能的应用扩展到科学和工程中更具挑战性的问题，圣地亚哥超级计算机中心（SDSC），与其供应商合作伙伴 Supermicro 密切合作，将部署Voyager 是一种高性能、创新的资源，用于在广泛的科学和工程领域进行人工智能研究，基于针对深度学习 (DL) 操作进行优化的人工智能处理器，Voyager 将成为同类系统中的首个。 NSF 资源组合将使研究人员有机会使用 PyTorch、Keras 和 Tensorflow 等成熟的深度学习框架来探索 Voyager 独特的硬件和软件，以实施卷积神经网络等深度学习技术。研究人员还可以使用专为 Voyager 创新 AI 架构构建的软件工具和库来开发自己的 AI 技术。该项目的结构为为期三年的测试平台阶段，然后是两年。年分配阶段。在测试平台阶段，SDSC 研究人员和合作者将与少数研究团队密切合作，评估 Voyager 创新深度学习 (DL) 硬件、专用编译器和系统库的性能。半年一次的研讨会将让团队聚集在一起分享经验教训，并开发知识和最佳实践，为未来在分配阶段获得访问权限的用户提供信息。在分配阶段，Voyager 将提供给参与 NSF 认为有价值的项目的研究人员。 - 批准的分配过程。从测试平台阶段吸取的经验教训用于开发文档、最佳实践、分配模型和用户支持策略，该项目将在分配阶段继续进行。与业界合作，探索 Voyager 等技术如何提高私营企业的全球竞争力，并培养下一代劳动力。SDSC 将在 UCSD 校园内的 SDSC 节能数据中心部署 Voyager，Voyager 将连接到多个数据中心。 100 Gbps 的高性能研究和教育网络由 SDSC 的全国公认的应用和系统专家团队提供支持。 Voyager 外部顾问委员会将协助招募早期用户并为该网络提供指导。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。