Collaborative Research: FET: Medium: Energy-Efficient Persistent Learning-in-Memory with Quantum Tunneling Dynamic Synapses

合作研究：FET：中：具有量子隧道动态突触的节能持久内存学习

• 批准号: 2208771
• 负责人: Gert Cauwenberghs
• 金额: $ 52.5万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2208771&HistoricalAwards=false
• 关键词: Collaborative; Research; FET; Medium; Energy

This research project investigates a framework that can significantly improve the energy-efficiency of training artificial intelligence (AI) systems using circuits and system architectures that are based on quantum-tunneling dynamic-analog-memory (DAM) devices. In 2019, the energy required to train a top-of-the-line AI system was more than the energy required to operate five US cars over their entire lifetime. The energy requirements for training large-scale AI systems have only gotten worse since to the point of being unsustainable. The proposed research aims to develop novel learning hardware that will make the training of ML and AI systems more energy sustainable. The project is also developing software tools for training AI systems that can be disseminated and adopted by the research community. The novel online learning and memory consolidation algorithms that are being developed in this project will be integrated with an openly shared, general-purpose neuromorphic cognitive computing platform available through the Neuroscience Gateway (NSG) Portal at the San Diego Supercomputer Center. In collaboration with Efabless Inc. the project is supporting open-source development of mixed-signal integrated circuits (IC) design tools that is being evaluated through in class-room instruction and projects.The technical activities of this research project are based on an ultra-energy-efficient synaptic element called Fowler-Nordheim Dynamic Analog Memory (FN-DAM) that can be easily fabricated on a standard integrated circuits process. The memory retention property of the synaptic element has been previously shown to be adaptive and can be traded-off with the energy required for synaptic updates. These FN-DAM properties are being explored within the context of the following research objectives: 1) Investigation into novel FN-DAM based neural network training and learning algorithms and architecture: Mechanisms are being explored that can connect the dynamics of FN-DAM array with the training formulations of standard convolutional neural network. Efficient one-shot continual online learning techniques are being investigated that exploit the dynamics of FN-DAM to improve the speed and robustness of learning. The framework is being used to explore connections between the FN-DAM based architectures with neuromorphic memory architectures that combines episodic-memories with incremental learning paradigms; 2) Investigation into novel FN-DAM based compute-in-memory and on-chip learning architectures: Analog compute-in-memory learning architectures are being investigated that integrate FN-DAM arrays with CMOS computing circuits and on-chip adaptation and learning strategies; 3) Validation of the FN-DAM based hardware-software co-design framework: The project is validating the co-design framework for achieving high energy-efficiency in neural network training using the NSF CISE Community Research Infrastructure (CRI) for large-scale neuromorphic cognitive computing developed and maintained at University of California at San Diego (UCSD). The project is also validating the energy-efficiency improvements that can be achieved using prototypes that will be fabricated in a standard integrated circuits process.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.

该研究项目研究了一种框架，该框架可以使用基于量子隧道动态模拟存储器（DAM）设备的电路和系统架构显着提高训练人工智能（AI）系统的能源效率。 2019 年，训练顶级人工智能系统所需的能量超过了五辆美国汽车整个生命周期所需的能量。自那以后，训练大规模人工智能系统的能源需求变得越来越严重，甚至达到了不可持续的程度。拟议的研究旨在开发新型学习硬件，使机器学习和人工智能系统的训练更加能源可持续。该项目还在开发用于训练人工智能系统的软件工具，可供研究界传播和采用。该项目正在开发的新型在线学习和记忆巩固算法将与一个开放共享的通用神经形态认知计算平台集成，该平台可通过圣地亚哥超级计算机中心的神经科学网关（NSG）门户获得。该项目与 Efabless Inc. 合作，支持混合信号集成电路 (IC) 设计工具的开源开发，这些工具正在通过课堂教学和项目进行评估。该研究项目的技术活动基于超-称为 Fowler-Nordheim 动态模拟存储器 (FN-DAM) 的节能突触元件，可以通过标准集成电路工艺轻松制造。突触元件的记忆保留特性先前已被证明是自适应的，并且可以与突触更新所需的能量进行权衡。这些 FN-DAM 特性正在以下研究目标的背景下进行探索： 1) 研究基于 FN-DAM 的新型神经网络训练和学习算法和架构：正在探索可以将 FN-DAM 阵列的动态与标准卷积神经网络的训练公式。正在研究高效的一次性持续在线学习技术，利用 FN-DAM 的动态来提高学习的速度和鲁棒性。该框架用于探索基于 FN-DAM 的架构与将情景记忆与增量学习范式相结合的神经形态记忆架构之间的联系； 2) 研究基于 FN-DAM 的新型内存计算和片上学习架构：正在研究将 FN-DAM 阵列与 CMOS 计算电路以及片上自适应和学习策略集成的模拟内存计算学习架构; 3) 基于 FN-DAM 的软硬件协同设计框架的验证：该项目正在验证协同设计框架，以使用 NSF CISE 社区研究基础设施 (CRI) 进行大规模神经网络训练，以实现高能效神经形态认知计算由加州大学圣地亚哥分校 (UCSD) 开发和维护。该项目还验证了使用将在标准集成电路工艺中制造的原型可以实现的能效改进。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进行评估，被认为值得支持审查标准。