Collaborative Research: ASCENT: 3D memristor convolutional kernels with diffusive memristor based reservoir for real-time machine learning

合作研究：ASCENT：3D 忆阻器卷积核，具有基于扩散忆阻器的存储库，用于实时机器学习

• 批准号: 2023752
• 负责人: Qiangfei Xia
• 金额: $ 130万
• 依托单位: University of Massachusetts Amherst
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2023752&HistoricalAwards=false
• 关键词: Collaborative; Research; ASCENT; 3D; memristor

Digital computers built with traditional electronic devices based on conventional architecture are ubiquitous in our daily lives. However, they are not able to keep up with the increasing demand for energy efficiency for data-intensive tasks, e.g., video streaming, because of intrinsic limitations. Memristor, a novel device whose resistance depends on their electrical history, has been proved to be able to overcome or avoid some of the limitations by performing computing at the same location where data is stored. With only one type of memristor, however, the demonstrated systems to date lack real-time learning capability in the hardware, which is required for both spatial and temporal information processing. The proposed project will develop a fundamentally new hardware system that integrates two different types of memristors and supporting circuits into three-dimensional (3D) networks. The new computing platform is expected to be more versatile, more compact, and more power efficient. The proposed research will lead to transformative hardware and technologies, contribute to the training of the nation’s high-caliber workforce, and hence reclaim the competitiveness and leadership of the IC industry of the U.S. The proposed project will be also integrated with STEM education through classroom teaching, summer camp for community college teachers, K-12 students, with the full participation of women and underrepresented minorities.The proposed project aims at experimentally implementing 3D memristor-based neural networks for real-time machine learning with high energy-speed efficiency. The specific objectives towards this goal are as follows: (1) to design and fabricate high-density nanoscale 3D-stacked passive arrays for parallel convolution operations for spatial feature extraction; (2) to enable reservoir computing with novel diffusive memristors in order to extract temporal patterns; (3) to develop learning algorithms co-designed with the hardware; and (4) to design and build an integrated system on printed circuit boards that physically integrates the 3D-stacked kernels, the diffusive memristor dynamic reservoir, the fully connected layer, and the auxiliary digital circuits for real-time video processing and classification. The success of the proposed work will not only provide an energy-area efficient hardware system with custom-tailored algorithms and software to realize real-time machine learning, more importantly, but it will also provide solutions to the biggest obstacles that hinder the processing-in-memory. This system could deliver a large computing throughput with small operating power and compact system size. More importantly, the pretrained convolution kernels and fixed connections of the dynamic reservoir could substantially reduce the training complexity, rendering the system suitable for real-time learning tasks like video classification with hardware learning circuits combined with co-designed algorithms and software.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.

基于传统架构的传统电子设备构建的数字计算机在我们的日常生活中无处不在。然而，它们无法满足数据密集型任务对能源效率日益增长的需求，例如，视频流，因为固有的限制。忆阻器是一种新型器件，其电阻取决于其电历史，已被证明能够通过在存储数据的同一位置执行计算来克服或避免某些限制。然而，由于只有一种类型的忆阻器，迄今为止所展示的系统在硬件中缺乏实时学习能力，这是空间和时间信息处理所需的。该项目将开发一种全新的硬件系统，将两种不同类型的忆阻器和支持电路集成到三维（3D）网络中。新的计算平台预计将更加通用，更加紧凑，更加节能。拟议的研究将导致变革性的硬件和技术，有助于培养国家的高素质劳动力，从而恢复美国IC行业的竞争力和领导地位。拟议的项目还将通过课堂教学，社区大学教师夏令营，K-12学生，在女性和代表性不足的少数族裔的充分参与下。拟议项目旨在实验性地实施基于3D忆阻器的神经网络，以实现高能效的实时机器学习。实现这一目标的具体目标如下：（1）设计和制造用于空间特征提取的并行卷积运算的高密度纳米级3D堆叠无源阵列;（2）使用新型扩散忆阻器实现储层计算以提取时间模式;（3）开发与硬件协同设计的学习算法;（4）开发与硬件协同设计的存储器阵列。以及（4）在印刷电路板上设计和构建集成系统，其物理地集成3D堆叠内核、扩散忆阻器动态库、全连接层和辅助数字电路，用于实时视频处理和分类。所提出的工作的成功不仅将提供一个具有定制算法和软件的能量区域有效的硬件系统来实现实时机器学习，更重要的是，它还将为阻碍内存处理的最大障碍提供解决方案。该系统可以提供一个大的计算吞吐量与小的操作电源和紧凑的系统尺寸。更重要的是，预先训练的卷积核和动态库的固定连接可以大大降低训练复杂度，使系统适合于实时学习任务，如具有硬件学习电路的视频分类，该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的评估来支持。影响审查标准。

项目成果

A fully hardware-based memristive multilayer perceptron.

完全基于硬件的忆阻多层感知器。

• 发表时间: 2022
• 期刊: Devices & Systems (MEMRISYS'22
• 作者: Fatemeh Kiani, Jun Yin

Engineering Tunneling Selector to Achieve High Non-linearity for 1S1R Integration

工程隧道选择器可实现 1S1R 集成的高非线性度

• DOI: 10.3389/fnano.2021.656026
• 发表时间: 2021
• 期刊: Frontiers in Nanotechnology
• 作者: Upadhyay, Navnidhi K.;Blum, Thomas;Maksymovych, Petro;Lavrik, Nickolay V.;Davila, Noraica;Katine, Jordan A.;Ievlev, A. V.;Chi, Miaofang;Xia, Qiangfei;Yang, J. Joshua
• 通讯作者: Yang, J. Joshua

Hierarchy of Event-Based Time-Surfaces Based on Diffusive Memristors with Uniform and Tunable Relaxation Time - A Preliminary Study

基于均匀且可调谐弛豫时间的扩散忆阻器的基于事件的时间表面层次结构 - 初步研究

• 发表时间: 2022
• 期刊: Devices & Systems (MEMRISYS'22
• 作者: Fan Ye, Fatemeh Kiani

Thousands of conductance levels in memristors integrated on CMOS

• DOI: 10.1038/s41586-023-05759-5
• 发表时间: 2023-03-30
• 期刊: NATURE
• 影响因子: 64.8
• 作者: Rao, Mingyi;Tang, Hao;Yang, J. Joshua
• 通讯作者: Yang, J. Joshua

Diffusive Memristors with Uniform and Tunable Relaxation Time for Spike Generation in Event‐Based Pattern Recognition

具有均匀且可调节弛豫时间的扩散忆阻器，用于基于事件的模式识别中的尖峰生成

• DOI: 10.1002/adma.202204778
• 发表时间: 2022
• 期刊: Advanced Materials
• 影响因子: 29.4
• 作者: Ye, Fan;Kiani, Fatemeh;Huang, Yi;Xia, Qiangfei
• 通讯作者: Xia, Qiangfei