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，并作为不断增长的数据科学领域的一部分，该领域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，宣布了一项涉及私营部门、学术界和公众的国家人工智能战略。除其他事项外，这一战略呼吁对人工智能研发进行投资，提供人工智能资源，并培训一支准备好人工智能的劳动力。研究人员越来越多地将机器学习(ML)技术应用于科学和工程问题，包括天文学、气候建模、极端系统管理、细胞生物学、高能物理、药物发现、社会科学、卫星图像分析等。除了算法和软件方面的进步外，人工智能系统的性能在很大程度上依赖于底层的硬件。评估针对人工智能算法优化的硬件是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越来越具有挑战性的问题，圣地亚哥超级计算机中心(SDSC)将与供应商合作伙伴SuperMicro密切合作，部署Voyager，这是一种高性能、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)操作优化的AI处理器，Voyager将成为NSF资源组合中第一个可用的同类系统。这将使研究人员有机会使用成熟的深度学习框架(如PyTorch、Kera和TensorFlow)探索Voyager独特的硬件和软件，以实施深度学习技术，如卷积神经网络(CNN)和生成性对手网络(GANS)。研究人员还将能够使用专门为Voyager的创新AI架构构建的软件工具和库来开发他们自己的AI技术。该项目的结构是为期三年的试验台阶段，然后是两年的分配阶段。在测试床阶段，SDSC研究人员和合作者将与少数研究团队密切合作，评估Voyager的创新深度学习(DL)硬件、专业编译器和系统库的性能。每半年举办一次的讲习班将使各小组聚集在一起，分享经验教训，并发展知识和最佳做法，以便向未来将在分配阶段获准使用的用户提供信息。在分配阶段，旅行者号将提供给研究人员，这些项目被美国国家科学基金会批准的分配过程视为有价值的项目。从测试阶段学到的经验用于开发文档、最佳实践、分配模型和用户支持战略。半年一次的讲习班继续在分配阶段举行。通过SDSC的AI技术实验室，该项目将与业界接触，探索像Voyager这样的技术如何提高私营部门公司的全球竞争力，并为下一代劳动力做好准备。SDSC将在UCSD园区的SDSC节能数据中心部署Voyager。Voyager将以100 Gbps的速度连接到多个高性能的研究和教育网络。Support Voyager是SDSC的一个全国公认的应用和系统专家团队。旅行者外部顾问委员会将帮助招募早期用户并为项目提供指导。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。