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

计算系统需要分析的数据量已经急剧增加到百亿亿次（即数十亿千兆字节）甚至更多。同时，由于人工智能（AI），特别是深度神经网络（DNN）的蓬勃发展，人们需要高性能、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的功率墙、传统冯-诺伊曼计算架构的内存墙以及基于dnn的超计算和内存密集型人工智能算法，现有的计算解决方案无法充分满足这些要求。该项目汇集了一个跨学科的研究小组，他们拥有材料科学、设备制造、集成电路设计、计算机架构和人工智能算法方面的专业知识，进行创新的设备-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实现高效的人工智能数据处理，以及芯片上的态势感知持续学习。该项目旨在显著提高人工智能数据处理的能效，其效率比目前最先进的图形处理单元（gpu）高出100倍。该项目将极大地惠及自动驾驶、机器人、个性化认知语音、智能互联健康等多个应用领域。该项目还将涉及教育和劳动力发展活动，包括K-12 STEM外展，本科/研究生培训，半导体课程开发，半导体行业实习指导，洁净室fab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族裔更广泛地参与微电子和半导体芯片行业。该项目将推进知识和开展跨层研究，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，以及人工智能算法探索，主要有三个相互交织的重点。Thrust 1将探索SOT材料（如MnPd3）中的非常规自旋，以及新颖的器件几何形状，以制造一种新的2端SOT- mram，同时提供无限的耐久性、纳秒级的编程时间、非常高的电池密度、无外部磁场的确定性编程、零泄漏和无挥发性。利用开发的2端SOT-MRAM， Thrust 2将设计并制作一个人工智能内存处理（PIM）芯片，以实现全数字的“内存稀疏乘法和累积（MAC）”操作，支持神经网络的前向和后向计算。根据协同设计方法，Thrust 3将首先研究自动网络架构搜索方法，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。这一推力将进一步开发新的pim友好型、计算和内存效率高、态势感知的持续学习算法，这些算法可以在学习新情况和用户特定数据的同时，最大限度地减少耗电的芯片重量更新（即内存写入）复杂性。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.