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

这项研究项目研究了一个框架，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来显著提高训练人工智能(AI)系统的能效。2019年，训练一流的人工智能系统所需的能源超过了运营五辆美国汽车一生所需的能源。自那以后，训练大规模人工智能系统的能源需求只会变得越来越差，以至于不可持续。拟议的研究旨在开发新的学习硬件，使ML和AI系统的训练更具可持续性。该项目还在开发培训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相结合，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)门户网站获得。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，该工具正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一种名为Fowler-Nordheim Dynamic Analog Memory(FN-DAM)的超高能效突触元件，该元件可以很容易地在标准集成电路工艺上制造。突触元件的记忆保持特性此前已被证明是自适应的，可以与突触更新所需的能量进行权衡。这些FN-DAM属性正在以下研究目标的背景下被探索：1)研究基于FN-DAM的新型神经网络训练和学习算法和体系结构：正在探索能够将FN-DAM阵列的动力学与标准卷积神经网络的训练公式联系起来的机制。人们正在研究有效的一次连续在线学习技术，以利用FN-DAM的动态特性来提高学习的速度和鲁棒性。该框架被用来探索基于FN-DAM的体系结构与将情景记忆与增量学习范例相结合的神经形态存储体系结构之间的联系；2)研究基于FN-DAM的新型内存中计算和片上学习体系结构：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相集成的模拟内存中计算学习体系结构；3)验证基于FN-DAM的软硬件协同设计框架：该项目正在使用NSF Cise社区研究基础设施(CRI)来验证协同设计框架，该框架用于在加州大学圣地亚哥分校(UCSD)开发和维护大规模神经形态认知计算，以实现神经网络训练的高能效。该项目还在验证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审查标准进行评估，被认为值得支持。

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.