CRII: SHF: IMMENSE: In-Memory Machine Learning using Sneak-Paths in Crossbars for Robustness and Energy Efficiency

摩尔定律和登纳德缩放法的消亡，导致了今天我们所经历的能源效率提高速度的放缓。这种放缓和冯·诺伊曼瓶颈对数据丰富和机器学习（ML）主导的未来产生了不利影响。使用忆阻交叉栅的内存计算已经成为一个有吸引力的选择，因为它可能比传统方法更节能，而且不会受到冯·诺伊曼瓶颈的影响。然而，现有的内存中依赖于记忆电阻的方法，如神经形态计算，存在导致输出不准确和能量利用率高的电阻漂移，并且对辐射损伤的鲁棒性不强。该项目寻求一种新的交叉栏计算方法，超越当前的神经形态方法，并利用新兴设备的3d交叉栏中的潜行路径来执行计算。这个项目带来的节能和抗辐射机器学习设备的社会影响将是巨大的。基于流的记忆电阻器交叉计算的低能量要求将实现节能ML系统的目标。由于这些设备对辐射损伤的抵抗力很强，因此它们可以在辐射丰富的环境中使用，比如太空。该项目旨在培养基于流的交叉计算科学的本科生和研究生，并为电子设计自动化领域的下一代劳动力做好准备。该项目的结果将在会议和研讨会上公开传播，以确保广泛接触学术界、政府和工业界的利益相关者。该项目旨在回答以下问题：如何为机器学习算法（如支持向量机和深度神经网络）设计内存交叉电路，以进行节能预测，同时对电阻漂移和辐射退化具有鲁棒性。该项目的目标是创建将程序映射到3-D横条的算法及其理论特征，以解释横条在数据结构背景下的计算能力，例如在正式方法中使用的不同类型的决策图。三个主要目标如下：(i)确定二部数据结构作为非核机器学习算法（如随机森林、线性和逻辑回归）的转换目标，（ii）确定忆阻器交叉棒的新空间抽象，以允许有效地利用忆阻器以降低能量和空间利用率，以及（iii）确定交叉棒抽象的函数组合运算符，以便将核化机器学习算法映射到交叉棒上。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。

The demise of both Moore's law and Dennard scaling has led to a slowdown in the rate of improvement in energy efficiency that is being experienced today. This slowdown and the von Neumann bottleneck adversely affect a data-rich and machine learning (ML) dominated future. In-memory computing using memristor crossbars has emerged as an attractive choice as it is likely to be more energy-efficient than traditional approaches and does not suffer from the von Neumann bottleneck. However, existing in-memory memristor-dependent methods, such as neuromorphic computing, suffer from resistance drift leading to inaccurate output and high energy utilization, and are not robust against radiation damage. This project seeks to pursue a new approach to crossbar computing that transcends current neuromorphic approaches and counterintuitively leverages sneak paths in 3-D crossbars of emerging devices for performing computations. The societal impact of energy-efficient and radiation-hardened machine learning devices enabled by this project will be enormous. The low energy requirements of flow-based memristor crossbar computing will enable the goal of energy-efficient ML systems. Since these devices are robust against radiation damage, they will allow their use in radiation-rich environments such as space. The project seeks to train undergraduate and graduate students in the science of flow-based crossbar computing and prepare an inclusive next-generation workforce in the area of electronic design automation. The results of the project will be publicly disseminated at conferences and workshops to ensure a wide reach to stakeholders in academia, government, and industry.The project aims to answer the following question: How to design in-memory crossbar circuits for machine learning algorithms such as support vector machines and deep neural networks for energy-efficient predictions while being robust against resistance drift and radiation degradation. The goal of this project is to create algorithms for mapping programs to 3-D crossbars and their theoretical characterizations that explain the computing capacity of crossbars in the context of data structures, such as different types of decision diagrams used in formal methods. The three primary objectives are as follows (i) identify bipartite data structures as transformation targets for non-kernel ML algorithms such as random forest, linear and logistic regression, (ii) determine novel spatial abstractions of memristor crossbar to allow efficient memristor utilization for lower energy and space utilization, and (iii) identify function composition operators for crossbar abstractions to enable mapping kernelized ML algorithms onto crossbars.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.