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

CRII：SHF：IMMENSE：使用交叉开关中的潜行路径实现稳健性和能源效率的内存中机器学习

• 批准号: 2245756
• 负责人: Sunny Raj
• 金额: $ 17.49万
• 依托单位: Oakland University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2245756&HistoricalAwards=false
• 关键词: CRII; SHF; IMMENSE; Memory; Machine

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.

摩尔定律和Dennard标度的消亡都导致了当今能源效率提高的速度放缓。这种放缓和冯·诺伊曼的瓶颈对数据丰富、机器学习(ML)占主导地位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，而且不会受到冯·诺伊曼瓶颈的影响。然而，现有的记忆记忆阻器依赖的方法，如神经形态计算，存在着电阻漂移导致输出不准确和能量利用率高的问题，并且不能很好地抵抗辐射损伤。这个项目试图寻求一种新的交叉开关计算方法，这种方法超越了目前的神经形态方法，并反直觉地利用新兴设备的3-D交叉开关中的偷偷路径来执行计算。该项目实现的节能和防辐射机器学习设备的社会影响将是巨大的。基于流量的忆阻器交叉开关计算的低能量需求将使节能ML系统的目标成为可能。由于这些设备对辐射损害具有很强的抵抗力，它们将允许在太空等辐射丰富的环境中使用。该项目旨在培训基于流量的纵横制计算科学方面的本科生和研究生，并在电子设计自动化领域培养一支包容各方的下一代劳动力队伍。该项目的结果将在会议和研讨会上公开传播，以确保学术界、政府和工业利益相关者的广泛接触。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)设计内存交叉开关电路，以实现节能预测，同时对抵抗漂移和辐射退化具有健壮性。这个项目的目标是创建将程序映射到3-D Crosbar及其理论特征的算法，以在数据结构的背景下解释Crosbar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)确定二分数据结构作为非核心ML算法的转换目标，例如随机森林、线性和Logistic回归；(Ii)确定新的忆阻器纵横杆的空间抽象，以实现更低的能量和空间利用率；以及(Iii)确定用于纵横杆抽象的函数组合运算符，以便能够将核化的ML算法映射到纵横杆上。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。