E2CDA: Type II: Non-Volatile In-Memory Processing Unit: Memory, In-Memory Logic and Deep Neural Network

E2CDA：II 类：非易失性内存中处理单元：内存、内存中逻辑和深度神经网络

基本信息

批准号：
2005209
负责人：
Deliang Fan
金额：
$ 11.7万
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：
2019
资助国家：
美国
起止时间：
2019-08-01 至 2021-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2005209&HistoricalAwards=false
关键词：
E2CDA Type II Non Volatile

项目摘要

The objective of this project is to explore leveraging emerging nanoscale spin-orbit torque magnetic random access memory (SOT-MRAM) to develop a non-volatile in-memory processing unit that could simultaneously work as non-volatile memory and a co-processor for next-generation energy efficient and high performance computing system. Such energy efficient in-memory computing system integrates logic and memory units by exploring innovations from emerging spintronic device technology to non-Von Neumann architecture, which is targeting to tackle power wall and memory wall bottlenecks in traditional computing system. It will be crucial for industry and academia to identify next-generation energy efficient and high performance computing platform design. The project also has education and outreach components including new curriculum in post-CMOS devices and circuits for undergraduate/graduate students, engineering outreach to diverse population and other underrepresented groups at the University of central Florida. The project will also directly involve minority and female graduate/ undergraduate students.The proposed research requires synergistic exploration spanning from device technology to architecture innovation. Specifically, it consists of three research thrusts: (i) exploring novel SOT-MRAM memory array that could implement in-memory logic (AND/OR/XOR) without add-on logic circuits; (ii) investigating non-volatile in-memory processing unit (MPU) architecture that could simultaneously work as nonvolatile memory and co-processor to pre-process raw data within memory to accelerate data/computing intensive applications without sacrificing memory capacity; (iii) exploring MPU to implement in-memory convolution to greatly reduce data communication and accelerate state-of-the-art deep learning convolutional neural networks.

该项目的目的是探索利用新兴的纳米级旋转轨道磁性磁性随机接入存储器（SOT-MRAM）开发一个非挥发性内存中的处理单元，该单元可以同时用作非挥发性存储器，并且是下一代能源高效和高性能计算系统的共同处理器。这样的节能内存内计算系统通过探索从新兴的Spintronic设备技术到非VON Neumann Architecture的创新来集成逻辑和内存单元，该创新的目标是解决传统计算系统中的电源墙和存储墙瓶颈。对于行业和学术界来说，确定下一代节能和高性能计算平台设计至关重要。该项目还拥有教育和外展成分，包括在频繁的学院设备中的新课程和本科/研究生的电路，工程宣传对多样化人群以及佛罗里达州中部大学的其他代表性不足的群体。该项目还将直接涉及少数民族和女性研究生/本科生。拟议的研究需要从设备技术到建筑创新的协同探索。具体而言，它由三个研究推力组成：（i）探索可以实现内存中逻辑（和/或/XOR）的新型SOT-MRAM内存数组，而无需附加逻辑电路；（ii）研究非挥发性内存处理单元（MPU）体系结构，可以同时用作非挥发性内存和协同处理器，以在内存中预处理原始数据，以加速数据/计算密集应用，而无需牺牲记忆能力；（iii）探索MPU实施内存卷积以大大减少数据通信并加速最先进的深度学习卷积神经网络。