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

在我们的日常生活中，基于传统架构的传统电子设备构建的数字计算机无处不在。然而，由于固有的限制，它们无法跟上数据密集型任务（例如视频流）对能源效率日益增长的需求。忆阻器是一种新型器件，其电阻取决于其电历史，已被证明能够克服或避免在存储数据的同一位置进行计算的一些限制。然而，只有一种类型的忆阻器，迄今为止演示的系统缺乏硬件的实时学习能力，这是空间和时间信息处理所必需的。拟议的项目将开发一个全新的硬件系统，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通用、更紧凑、更节能。拟议的研究将带来变革性的硬件和技术，有助于培养美国高素质的劳动力，从而恢复美国集成电路产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，以及妇女和少数族裔的充分参与，与STEM教育相结合。该项目旨在通过实验实现基于3D忆阻器的神经网络，用于高能量-速度效率的实时机器学习。实现这一目标的具体目标如下：(1)设计和制造高密度纳米级3d堆叠无源阵列，用于空间特征提取的并行卷积操作；(2)利用新型扩散忆阻器进行储层计算，提取时间模式；(3)开发与硬件协同设计的学习算法；(4)在印刷电路板上设计搭建集成系统，将3d堆叠核、扩散忆阻器动态库、全连接层、辅助数字电路物理集成，实现实时视频处理和分类。这项工作的成功不仅将提供一个具有定制算法和软件的节能硬件系统，以实现实时机器学习，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。该系统能够以较小的运行功率和紧凑的系统尺寸提供较大的计算吞吐量。更重要的是，预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，使系统适合于视频分类等实时学习任务，采用硬件学习电路结合协同设计的算法和软件。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.