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

摩尔定律和登纳德比例的消亡导致了今天正在经历的能源效率提高速度的放缓。这种放缓和冯·诺依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。使用忆阻器交叉开关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，并且不会受到冯诺依曼瓶颈的影响。然而，现有的存储器内忆阻器依赖的方法，如神经形态计算，遭受电阻漂移，导致不准确的输出和高能量利用率，并且对辐射损伤不鲁棒。该项目旨在寻求一种新的交叉计算方法，超越当前的神经形态方法，并违反直觉地利用新兴设备的3-D交叉中的潜路径进行计算。该项目实现的节能和抗辐射机器学习设备的社会影响将是巨大的。基于流的忆阻器交叉开关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具有鲁棒性，因此它们将允许在太空等辐射丰富的环境中使用。该项目旨在培养本科生和研究生在基于流的交叉计算科学，并准备在电子设计自动化领域的包容性的下一代劳动力。 该项目的成果将在会议和研讨会上公开传播，以确保学术界、政府和工业界的利益相关者广泛参与。该项目旨在回答以下问题：如何设计用于支持向量机和深度神经网络等机器学习算法的内存交叉开关电路，以实现节能预测，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算法，用于将程序映射到3-D crossbar及其理论特征，解释数据结构背景下crossbar的计算能力，例如形式方法中使用的不同类型的决策图。三个主要目标如下：（i）识别二分数据结构作为非内核ML算法（诸如随机森林、线性和逻辑回归）的变换目标，（ii）确定忆阻器交叉杆的新颖空间抽象以允许高效忆阻器利用以实现较低的能量和空间利用，以及（iii）确定交叉开关抽象的函数组合运算符，以将内核化ML算法映射到交叉开关上。该奖项反映了NSF的法定使命，并被视为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，

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.