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.

通过计算系统分析所需的数据量越来越大，对Exascale（即数十亿GB）及以后。同时，由于人工智能（AI）的繁荣，尤其是深神经网络（DNN），因此需要高性能，高效，快速和基于自适应的AI大数据处理系统。但是，由于基于硅的半导体设备中的电壁，传统的von-neuman计算体系结构中的内存壁以及超计算和基于内存的DNN基于DNN的AI算法，因此由于现有的计算解决方案无法充分满足这些要求。该项目汇集了跨学科的研究人员，这些研究人员涵盖了材料科学，设备制造，集成电路设计，计算机架构和AI算法的专家，以进行创新的设备电路 - 叠加符号，以开发AI处理中的AI处理（AI-PIM）系统，从而可以实现效率的AI，从而实现效率AI，以实现效率的AI，以实现有效的Memelitime Memainity Memberiate Memainity Memainity Memainity Memainity Memagnite Memainity and Imerage Memainity Memainity and cor war and cor par and cor par and colagatie for cor war片上持续学习。该项目的目标是显着提高AI数据处理能源效率，效率高100倍，而最先进的图形处理单元（GPU）。该项目将极大地受益于各个应用领域，例如自动驾驶，机器人技术，个性化认知语音和智能连接的健康等。该项目还将涉及教育和劳动力发展活动，包括K-12 STEM外展，本科/研究生培训，半导体课程开发，半导体行业，半导体行业，半导体行业，清洁室内式织物，先进的巡回赛。这也将鼓励女性和代表性不足的少数民族在微电子和半导体芯片行业中的广泛参与。该项目将推进知识和进行跨层研究，从新兴的自旋轨道扭矩磁性随机访问记忆（SOT-MRAM）材料，设备，电路，体系结构到AI算法探索，并具有三个主要交互式推力。推力1将探索SOT材料中的非常规的旋转，例如MNPD3和新型设备几何形状，以制造2端SOT-MRAM的新设计，该设计同时提供无限的耐力，纳米方面的纳米方案时间，非常高的细胞密度，非常高的细胞密度，没有外部磁场，没有外部磁场，零泄漏，零泄漏，零泄漏。推力2利用开发的2端SOT-MRAM，将设计和磁带插入Memory（PIM）芯片的AI处理，以实现完全数字的“内存中稀疏乘法和积累（MAC）”操作，以支持神经网络的前进和后退计算。遵循共同设计的方法，Thrust 3将首先研究自动化网络体系结构搜索方法，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。这一推力将进一步发展新颖的PIM友好，计算和记忆力，情境意识到的持续学习算法，这些算法可以最大程度地减少持芯片的重量更新（即记忆写入）的复杂性，同时学习新的状况和用户特定的数据，这些奖项反映了NSF的法定任务和良好的范围，这是通过评估良好的范围来进行的，这是通过评估的范围来进行的。