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个框架，该框架可以通过基于量子隧道动态 - 肛门 - 模拟 - 记忆（DAM）设备的电路和系统体系结构来显着提高训练人工智能（AI）系统的能效。在2019年，训练顶级AI系统所需的能量超过了在整个一生中运营五辆美国汽车所需的能量。训练大规模AI系统的能源需求只是变得更糟，因为它是不可持续的。拟议的研究旨在开发新颖的学习硬件，使ML和AI系统的培训更加可持续。该项目还正在开发用于培训AI系统的软件工具，该系统可以由研究界传播和采用。该项目中正在开发的新型在线学习和记忆合并算法将与通过神经科学网关（NSG）门户网站（NSG）门户网站（San Diego Supercuter Center）提供公开共享的通用神经形态认知计算平台。该项目与Efabless Inc.合作，支持通过课堂教学和项目进行评估的混合信号集成电路（IC）设计工具的开源开发。该研究项目的技术活动基于一个超能量的突触元素，称为Fowler-Nordheim Nordheim Dynamig Memory（FN-DAM），可以轻松地构成一体式的流程。先前已证明突触元件的内存保留属性是自适应的，可以通过突触更新所需的能量进行交易。这些FN-DAM属性正在以下研究目标的背景下进行：1）研究基于FN-DAM的新型神经网络培训和学习算法和体系结构：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形式联系起来的机制。正在研究有效的一声持续在线学习技术，以利用FN-DAM的动态来提高学习的速度和稳健性。该框架被用来探索基于FN-DAM的架构与神经形态记忆体系结构之间的连接，该架构将情节词与增量学习范式相结合； 2）研究基于新型FN-DAM的记忆和片上学习体系结构：正在研究模拟计算中的内存学习体系结构，将FN-DAM阵列与CMOS计算电路和芯片适应和学习策略相结合； 3）验证基于FN-DAM的硬件软件共同设计框架：该项目正在验证使用NSF CISE社区研究基础设施（CRI）在加州大学开发和维护的大型神经性认知计算中，以实现神经网络培训的高能效框架。该项目还正在验证可以使用标准综合电路流程中制造的原型来实现的能源效率改进。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子评估来获得支持的，并具有更广泛的影响。