PFI-RP: Resilient and Energy-Efficient Memory Chips for Enhanced Mobile AI and Personalized Machine Learning

PFI-RP：用于增强移动人工智能和个性化机器学习的弹性和节能内存芯片

• 批准号: 2345655
• 负责人: Shan Wang
• 金额: $ 100万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-03-01 至 2027-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2345655&HistoricalAwards=false
• 关键词: PFI; RP; Resilient; Energy; Efficient

The broader impact of this Partnerships for Innovation - Research Partnerships (PFI-RP) project extends across scientific, technological, and societal realms. By focusing on the development of a resilient, fast-switching, and energy-efficient magnetoresistive random access memory (MRAM), this research aims to revolutionize the landscape of mobile artificial intelligence (AI). Unlike current memory options, this innovation is like a supercharged engine for your smartphone or smartwatch, making them smarter and much longer lasting with just one charge. This means not just faster processing, but also a smaller, personalized AI right in your device. The research tackles thermal challenges, design intricacies and nanofabrication to make this technology work seamlessly, with the goal of creating jobs and preparing students for the future. Think of it as upgrading the brain of your gadgets. Once developed, this AI chip could be used by companies like Apple or Tesla to enable brand-new applications. The demand for this kind of tech is huge and growing, and it could lead to a major breakthrough in how we use AI in our daily lives, while making our devices and privacy securely protected. The project will also empower a diverse group of students with crucial skills for innovation and semiconductor fabrication.The proposed project aims to tackle critical technical hurdles in the development of the next generation of MRAM materials and devices, focusing on enhancing thermal robustness, achieving fast and enduring switching, and ensuring sustainable energy efficiency. With a specific focus on applications in mobile AI, the research seeks innovative solutions that address challenges currently limiting the widespread adoption of MRAM technologies. In the evolving landscape of mobile computing, where AI algorithms promise transformative user experiences, data security and privacy concerns are paramount. Traditional AI models, trained extensively in the cloud, pose risks of compromising personal user information. Moreover, their deployment on mobile devices often results in suboptimal performance for certain user groups. This project will advance the state of the art in mobile AI by leveraging nonvolatile MRAM-based associative memories at the hardware level and embeddings-based neural networks at the software level. By doing so, the research envisions a future where AI applications on mobile devices are not only efficient (preserving battery power) but also secure (keeping private data locally on your own device), ensuring a more inclusive and responsive experience for users of all kinds of AI tools.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.

这种伙伴关系对创新 - 研究合作伙伴关系（PFI -RP）项目的广泛影响扩展了科学，技术和社会领域。通过关注弹性，快速切换和节能的磁磁性随机访问记忆（MRAM），该研究旨在彻底改变移动人工智能（AI）的景观。与当前的内存选项不同，这项创新就像您的智能手机或智能手表的增压引擎一样，使它们更聪明，更长的时间持续使用，只需一笔费用即可。这不仅意味着更快的处理，而且还意味着您的设备中的一个较小的个性化AI。该研究解决了热挑战，设计复杂性和纳米型制作，以使这项技术无缝地工作，目的是创造就业机会并为学生做好准备。将其视为升级小工具的大脑。一旦开发，苹果或特斯拉等公司就可以使用这种AI芯片来启用全新的应用程序。对这种技术的需求庞大且不断增长，这可能会导致我们如何在日常生活中使用AI的重大突破，同时使我们的设备和隐私受到安全保护。该项目还将赋予一群具有创新和半导体制造技能的多样化的学生。拟议的项目旨在解决下一代MRAM材料和设备的发展，以解决关键的技术障碍，重点是增强热鲁棒性，快速实现快速和持久的开关，并确保可持续的能源效率。该研究特别关注移动AI中的应用程序，寻求创新的解决方案，以应对目前限制MRAM技术广泛采用的挑战。在移动计算的不断发展的景观中，AI算法有望变革性用户体验，数据安全和隐私问题至关重要。传统的AI模型在云中进行了广泛的培训，构成了损害个人用户信息的风险。此外，它们在移动设备上的部署通常会导致某些用户组的次优性能。该项目将通过在软件级别的硬件级别和基于嵌入式的神经网络上利用基于MRAM的关联记忆来推动移动AI的最新状态。 By doing so, the research envisions a future where AI applications on mobile devices are not only efficient (preserving battery power) but also secure (keeping private data locally on your own device), ensuring a more inclusive and responsive experience for users of all kinds of AI tools.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.