ACED Fab: Ultrafast, low-power AI chip with a new class of MRAM for learning and inference at edge

ACED Fab：超快、低功耗 AI 芯片，配备新型 MRAM，用于边缘学习和推理

• 批准号: 2314591
• 负责人: Shan Wang
• 金额: $ 55万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2314591&HistoricalAwards=false
• 关键词: ACED; Fab; Ultrafast; low; power

The collaborative team of this project under Advanced Chip Engineering Design and Fabrication (ACED Fab) program will be working on an exciting advancement in the field of artificial intelligence (AI) for edge computing, such as secured machine learning based on personalized or sensitive data in smartphones (a type of edge devices) without resorting to a server at a remote data center. The project introduces a new class of Magnetoresistive Random Access Memory (MRAM) called Spin-transfer torque (STT) Assisted Spin-orbit torque (SOT)-MRAM (SAS-MRAM) which features ultralow power consumption and ultrafast write speeds. By co-designing SAS-MRAM with CMOS circuits, the project aims to create energy-efficient edge AI systems. SAS-MRAM's non-volatile nature eliminates standby leakage power, making edge-AI chips more energy-efficient at the system level compared to existing approaches using Static Random Access Memory (SRAM). The project’s activities extend beyond technological advancements, with plans of K-12 STEM outreach, undergraduate/graduate training, curriculum development in innovation and entrepreneurship, and broadening participation of underrepresented minority groups in the microelectronics STEM field and semiconductor industry. The team’s efforts in education and inclusivity will contribute to a diverse and innovative future of the microelectronics industry. The new SAS-MRAM with ultralow power and ultrafast write speed will be co-designed with CMOS circuits for energy-efficient edge AI applications. The SAS-MRAM will be fabricated on top of a TN40G CMOS die through a custom back-end-of-line (BEOL) process. The team will systematically perform micromagnetic simulation and HSpice simulation to build Process Development Kits (PDKs) required for co-designing SAS-MRAM and CMOS circuits. Furthermore, the project will leverage SAS-MRAM to design, optimize, and tape-out an In-Memory Computing (IMC) chip prototype for edge-AI, which could implement both on-chip inference and training computation. Finally, the project will develop new continual learning algorithms that could minimize the memory weight updates (i.e., memory writes) and computing complexity, allowing the AI system to learn new data without forgetting previously learned knowledge. The resulting edge-AI chips will be significantly more energy-efficient at system level than the prevalent counterparts based on SRAM due to zero standby leakage power for non-volatile MRAM. On-device training/learning based on SAS-MRAM is potentially ultrafast due to lower latency from denser bit cells and multi-bit writing with shared SOT write lines. The project can potentially revolutionize edge AI devices and systems by leveraging SAS-MRAM and in-memory computing to create energy-efficient AI systems with improved performance.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.

该项目在人工智能领域（AI）（用于边缘计算）中的合作芯片工程设计和制造，例如基于智能手机的个性化或敏感数据（一种带平静的旋转转移扭矩）的确保机器学习（一种边缘设备）（STT） ）辅助自旋轨道扭矩（SOT） - MRAM（SAS-MRAM），通过与CMOS Circuits MS共同设计SAS-MRAM，以创建能源效率的Edge Edge AI Systems。挥发性自然消除了备用泄漏功率，使用静态随机访问记忆（SRAM）的活动超出了技术进步，使Edge-Ai芯片更加能量能量 - 以静态随机访问记忆为单位。 /研究生培训，开发Inntrepreepreneurs精神，并扩大了微观的少数群体，而微型群体和半导体行业的努力。 - 使用CMOS CMOS CMOS CMOS CMOS CMOS CMOS CMOS CMOS CMOS CMOS CITH EDGE AI应用程序进行设计。 PDK）ING SAS-MRAM和CMOS电路所需 - 芯片推理和训练计算。基于SAS-MRAM的培训/学习可能是由于较密集的位单元中的较低而具有超快的速度，并且具有共享t写入线的多位。能量能量效果具有提高的性能。该奖项反映了NSF'SF'SFFLY的使命，并通过基金会的知识分子和更广泛的影响评估标准对评估进行了支持。

项目成果

Slimmed Asymmetrical Contrastive Learning and Cross Distillation for Lightweight Model Training

用于轻量级模型训练的精简非对称对比学习和交叉蒸馏

• 发表时间: 2023
• 期刊: Thirty-seventh Conference on Neural Information Processing Systems
• 作者: Meng, Jian;Yang, Li;Lee, Kyungmin;Shin, Jinwoo;Fan, Deliang;Seo, Jae-sun
• 通讯作者: Seo, Jae-sun