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

基本信息

批准号：
2328803
负责人：
Deliang Fan
金额：
$ 70万
依托单位：
Arizona State 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 2024-02-29
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2328803&HistoricalAwards=false
关键词：
Collaborative Research FuSe Efficient Situation

项目摘要

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。然而，由于硅基半导体器件中的电源墙和传统的存储墙，现有的计算解决方案无法充分满足对高性能、高效、快速和自适应的基于人工智能的大数据处理系统的需求。冯·诺依曼该项目汇集了跨学科的研究人员小组，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的专业知识，以进行创新。器件-电路-算法协同设计，用于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器技术来实现高效的人工智能数据处理以及片上态势感知该项目的目标是显着提高人工智能数据处理的能源效率，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种应用领域，例如自动驾驶、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外展、本科生/研究生培训、半导体课程开发、半导体行业实习指导、洁净室很棒的该项目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，并开展跨层研究，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、架构到 AI 算法探索，三个主要方向相互交织，Thrust 1 将探索 SOT 材料中的非常规自旋，例如： MnPd3 和新颖的器件几何结构可制造新设计的 2 端子 SOT-MRAM，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将利用开发的2端子SOT-MRAM设计并流片一款AI内存处理（PIM）芯片，以实现全数字化“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，遵循协同设计方法，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。我们的 AI-PIM 系统约束将进一步开发新颖的 PIM 友好型、计算和内存效率高、情境感知的连续学习算法，可以最大限度地减少片上权重更新（即内存）的耗电。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。