Exploiting Metal-Insulator-Transition in Strongly Correlated Oxides as Neuron Device for Neuro-Inspired Computing

利用强相关氧化物中的金属-绝缘体转变作为神经元设备进行神经启发计算

• 批准号: 1701565
• 负责人: Shimeng Yu
• 金额: $ 36万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1701565&HistoricalAwards=false
• 关键词: Exploiting; Metal; Insulator; Transition; Strongly

A radical shift in computing paradigm towards the neuro-inspired computing is attractive for performing data-intensive applications such as image/speech recognitions. The neuro-inspired architecture leverages the distributed computation in the neuron nodes and the localized storage in the synaptic elements. The neuron node today is generally implemented by tens of silicon transistors. Compared to the crossbar array of synaptic elements, the silicon neuron is power-hungry and area-inefficient, thereby reducing the parallelism of computing system. In such context, how to design a single device that can efficiently emulate the neuronal behavior (e.g. integrate-and-fire) is critical to the neuromorphic hardware design. This project aims to exploit the metal-insulator-transition phenomenon in strongly correlated oxides as a compact neuron node that can self-oscillate, namely oxide neuron, to overcome the aforementioned limitations of silicon neuron. The proposed research will have a profound impact on the society that is embracing the artificial intelligence. For instance, a compact design of neuromorphic hardware may enable intelligent information processing on power-efficient mobile platforms, e.g. autonomous vehicle, personalized healthcare, wearable devices, and smart sensors. The objective of the research and education integration is to train undergraduate/graduate students and next-generation workforce with interdisciplinary skills. The cross-layer nature of this project ranging from materials engineering, semiconductor device, circuit-device interaction and artificial neural network provides an ideal platform for this educational goal. The project also plans to engage minority and unrepresentative students in research. Technology transfer will be performed through video or on-site seminars and student internships with industrial collaborators.The goal of this research is to advance the artificial neuron device design by exploiting the volatile and threshold switching behavior in strongly correlated oxides, with the purpose of significantly reducing the area and energy of the neuron node, and making it compatible for the integration with crossbar array of resistive synaptic elements. The scope of the project is to explore various material systems of the strongly correlated oxides, in particular, NbO2 and SmNiO3 to demonstrate the self-oscillation behavior in the artificial neuron node. When such oxide device is connected with a series synaptic element whose resistance is within the on/off dynamic range of the oxide device, the node voltage between the oxide device and the synaptic element will start self-oscillation, and the oscillation frequency represents the synaptic conductance. This project aims to explore such self-oscillation to emulate the integrate-and-fire neuronal behavior. To achieve the aforementioned research goal, device fabrication, physical and electrical characterization, device modeling, and circuit-device co-design will be performed to demonstrate the feasibility of the concept and further optimize the device performance. The intellectual significance of this project is two folded. From the fundamental science perspective, the physical switching mechanism of metal-insulator-transition in strongly correlated oxides will be investigated. From the applied engineering perspective, the oxide neuron device will be integrated with the resistive crossbar array for demonstration of a neural network for solving a practical problem, i.e. the image pattern classification.

计算范式向神经启发计算的根本转变对于执行诸如图像/语音识别的数据密集型应用是有吸引力的。神经启发架构利用神经元节点中的分布式计算和突触元件中的本地化存储。今天的神经元节点通常由数十个硅晶体管实现。相对于突触元件的交叉阵列，硅神经元是功耗和面积低效的，从而降低了计算系统的并行性。在这种情况下，如何设计一个单一的设备，可以有效地模拟神经元的行为（例如集成和发射）是至关重要的神经形态硬件设计。本计画旨在利用强关联氧化物中的金属-绝缘体-转变现象，作为一个可自激振荡的紧凑型神经元节点，即氧化物神经元，以克服硅神经元的上述限制。拟议的研究将对拥抱人工智能的社会产生深远的影响。例如，神经形态硬件的紧凑设计可以在功率高效的移动的平台（例如，自主车辆、个性化医疗保健、可穿戴设备和智能传感器）上实现智能信息处理。研究和教育一体化的目标是培养本科生/研究生和具有跨学科技能的下一代劳动力。该项目的跨层性质，包括材料工程，半导体器件，电路-器件相互作用和人工神经网络，为这一教育目标提供了理想的平台。该项目还计划让少数民族和无代表性的学生参与研究。技术转移将通过视频或现场研讨会以及与工业合作者的学生实习来进行。本研究的目标是通过利用强相关氧化物中的挥发性和阈值开关行为来推进人工神经元器件设计，目的是显着减少神经元节点的面积和能量，并且使其与电阻性突触元件的交叉阵列的集成兼容。该项目的范围是探索强关联氧化物的各种材料系统，特别是NbO 2和SmNiO 3，以证明人工神经元节点中的自振荡行为。当这种氧化物器件与串联突触元件连接时，其电阻在氧化物器件的开/关动态范围内，氧化物器件和突触元件之间的节点电压将开始自振荡，并且振荡频率表示突触电导。本计画旨在探讨这种自激振荡，以模拟整合与激发的神经元行为。为了实现上述研究目标，将进行器件制造、物理和电学表征、器件建模和电路-器件协同设计，以证明概念的可行性并进一步优化器件性能。这个项目的智力意义是双重的。从基础科学的角度，研究强关联氧化物中金属-绝缘体-转变的物理开关机制。从应用工程的角度来看，氧化物神经元器件将与电阻交叉阵列集成，用于演示解决实际问题的神经网络，即图像模式分类。