Collaborative Research: FuSe: Efficient Situation-Aware AI Processing in Advanced 2-Terminal SOT-MRAM

合作研究：FuSe：先进 2 端子 SOT-MRAM 中的高效态势感知 AI 处理

• 批准号: 2328804
• 负责人: Shan Wang
• 金额: $ 70万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2328804&HistoricalAwards=false
• 关键词: Collaborative; Research; FuSe; Efficient; Situation

The amount of data required to be analyzed by computing systems has been increasing drastically to exascale (i.e., billions of gigabytes) and beyond. Meanwhile, owing to the boom in artificial intelligence (AI), especially Deep Neural Network (DNN), there is a need for high performance, efficient, fast, and adaptive AI-based big data processing systems. However, those requirements are not sufficiently met by existing computing solutions due to the power-wall in silicon-based semiconductor devices, memory-wall in traditional Von-Neuman computing architecture, and ultra computation- and memory-intensive DNN-based AI algorithms. This project brings together an interdisciplinary group of researchers, with expertise spanning from material science, device fabrication, integrated circuit design, computer architecture, and AI algorithms to undertake innovative device-circuit-algorithm co-design for developing an AI Processing-In-Memory (AI-PIM) system that could leverage the emerging non-volatile magnetic memory technology to implement efficient AI data processing, as well as situation-aware on-chip continual learning. This project targets to significantly improve the AI data processing energy efficiency, with 100X higher efficiency than that of state-of-the-art Graph Processing Units (GPUs). The project will greatly benefit various application areas, such as autonomous driving, robotics, personalized cognitive speech, and smart connected health, etc. This project will also involve education and workforce development activities, including K-12 STEM outreach, undergraduate/graduate training, curriculum development in semiconductor, semiconductor industry internship mentoring, cleanroom fab internships, advance integrated circuit design courses. It will also encourage broader participation of female and under-represented minorities in the microelectronics and semiconductor chip industry. This project will advance knowledge and conduct cross-layer research spanning from emerging Spin-Orbit Torque Magnetic Random Access Memory (SOT-MRAM) material, device, circuit, architecture, to AI algorithm exploration with three main interweaved thrusts. Thrust 1 will explore unconventional spins in SOT materials, e.g., MnPd3, and novel device geometry to fabricate a new design of 2-terminal SOT-MRAM, which simultaneously delivers unlimited endurance, nano-seconds programming time, very high cell density, deterministic programming without external magnetic field, zero leakage, and non-volatility. Leveraging the developed 2-terminal SOT-MRAM, Thrust 2 will design and tape-out an AI Processing-in-Memory (PIM) chip to implement fully digital ‘in-memory sparse multiplication-and-accumulation (MAC)’ operations that support both forward and backward computations of neural networks. Following a co-design methodology, Thrust 3 will first investigate automated network architecture search methods to construct AI model best suitable for given situation while considering our AI-PIM system constraint. This thrust will further develop novel PIM-friendly, compute- and memory-efficient, situation-aware continual learning algorithms that could minimize the power-hungry on-chip weight update (i.e., memory write) complexity, while learning new situation- and user-specific data.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.

计算系统需要分析的数据量已急剧增加到百亿亿级（即数十亿千兆字节）甚至更高。同时，由于人工智能（AI）特别是深度神经网络（DNN）的蓬勃发展，需要高性能、高效、快速、自适应的基于人工智能的大数据处理系统。然而，由于硅基半导体器件中的功率墙、传统冯诺依曼计算架构中的内存墙以及基于超计算和内存密集型 DNN 的 AI 算法，现有的计算解决方案并不能充分满足这些要求。该项目汇集了跨学科的研究人员小组，他们拥有材料科学、器件制造、集成电路设计、计算机架构和人工智能算法的专业知识，进行创新的器件-电路-算法协同设计，以开发人工智能内存处理（AI-PIM）系统，该系统可以利用新兴的非易失性磁存储技术来实现高效的人工智能数据处理和态势感知 片上持续学习。该项目的目标是显着提高人工智能数据处理能源效率，其效率比最先进的图形处理单元（GPU）高100倍。该项目将极大地惠及各个应用领域，如自动驾驶、机器人、个性化认知语音和智能互联健康等。该项目还将涉及教育和劳动力发展活动，包括K-12 STEM推广、本科生/研究生培训、半导体课程开发、半导体行业实习指导、洁净室晶圆厂实习、高级集成电路设计课程。它还将鼓励女性和代表性不足的少数群体更广泛地参与微电子和半导体芯片行业。该项目将推进知识发展并进行跨层研究，涵盖新兴的自旋轨道扭矩磁随机存取存储器（SOT-MRAM）材料、器件、电路、架构，以及三个主要相互交织的人工智能算法探索。 Thrust 1 将探索 SOT 材料（例如 MnPd3）中的非常规自旋和新颖的器件几何形状，以制造新设计的 2 端子 SOT-MRAM，该设计同时提供无限的耐用性、纳秒级编程时间、非常高的单元密度、无需外部磁场的确定性编程、零泄漏和非易失性。 Thrust 2将利用已开发的2端子SOT-MRAM，设计并流片一款人工智能内存处理（PIM）芯片，以实现全数字化的“内存中稀疏乘法累加（MAC）”运算，支持神经网络的前向和后向计算。按照协同设计方法，Thrust 3 将首先研究自动化网络架构搜索方法，以构建最适合给定情况的 AI 模型，同时考虑我们的 AI-PIM 系统约束。这一推动力将进一步开发新颖的 PIM 友好、计算和内存高效、情境感知的持续学习算法，可以最大限度地减少耗电的片上权重更新（即内存写入）复杂性，同时学习新的情境和用户特定数据。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。