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.

摩尔定律和丹纳德·比尔辛（Dennard Scaling）的灭亡导致今天经历的能源效率的提高速度放缓。这种放缓和冯·诺伊曼瓶颈会不利地影响数据丰富的机器学习（ML）的未来。使用Memristor Crossbars的内存计算已成为一个有吸引力的选择，因为它可能比传统方法更节能，并且不会遭受Von Neumann瓶颈的困扰。然而，现有的内存中备忘录依赖性方法（例如神经形态计算）遭受了阻力漂移，导致输出不准确和高能量利用，并且对放射损害并不强大。该项目旨在追求一种新的方法来超越当前的神经形态方法，并在新兴设备的3-D横杆中偷偷摸摸地偷偷摸摸地进行计算。该项目实现的节能和辐射式机器学习设备的社会影响将是巨大的。基于流动的回忆录横梁计算的低能需求将实现能源有效的ML系统的目标。由于这些设备可抵抗辐射损坏，因此它们将允许在辐射富的环境（例如空间）中使用。该项目旨在培训基于流动的横杆计算科学科学的本科生和研究生，并在电子设计自动化领域准备一名包容性的下一代劳动力。 该项目的结果将在会议和研讨会上公开散布，以确保与学术界，政府和行业的利益相关者广泛覆盖。该项目旨在回答以下问题：如何设计机器内的跨键跨电路，以供机器学习算法，诸如支持向量机器和深度神经网络的能源效能的预测，同时进行抗抑郁和耐药性，以置于良好的范围内。该项目的目的是创建用于将程序映射到3-D横杆及其理论特征的算法，以解释在数据结构的背景下横杆的计算能力，例如正式方法中使用的不同类型的决策图。这三个主要目标如下（i）确定两分的数据结构是非内核ML算法的转换目标，例如随机森林，线性和逻辑回归，（ii）确定磁带横梁的新型空间抽象，以允许有效地使用较低能量和太空的启发器，以及（IIIII）识别功能构造的较低能量的启发器，以及III III型功能运算师，并允许MIRBAR，以及（III）识别功能运算师。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的影响审查标准通过评估来支持的。