FET: Small: 2D Material Compound Synapse Arrays for Robust In-Memory and Neuromorphic Computing (2DNeuro)

FET：小型：2D 材料复合突触阵列，用于强大的内存和神经形态计算 (2DNeuro)

• 批准号: 2001107
• 负责人: Ivan Sanchez Esqueda
• 金额: $ 50万
• 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: 2019
• 资助国家: 美国
• 起止时间: 2019-08-16 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2001107&HistoricalAwards=false
• 关键词: FET; Small; 2D; Material; Compound

Novel computing systems that emulate the brain have received significant attention from the scientific and engineering community, as they can help analyze large amounts of information while consuming only small amounts of energy. Significant advances have been made on these neuro-inspired systems, but there are still limitations that prevent their practical application on portable devices. This work will investigate novel materials and designs that can alleviate these limitations, enabling a better execution of critical mathematical functions. The scientific breakthroughs resulting from this work will support robust, efficient, and massively parallel computing for data-intensive machine learning applications. A profound societal impact will be achieved by laying the foundations for novel computing hardware that will support cognitive systems and artificial intelligence of tomorrow. Another objective of this project is to integrate research and education by training students (including graduate and undergraduate researchers), by developing educational tools in form of web-based interactive platforms, and by supporting a graduate-level course in the topic of novel integrated memory and neuro-inspired computing architectures.This project will establish the foundations of new storage technology based on two-dimensional (2-D) atomically-thin materials, extending the practical engineering limits of neuro-inspired systems. These storage devices, known as memristors, can be programmed to different states, based on an externally applied stimulus. When organized in specific configurations, memristor arrays can be used to implement complex functions that are used by the system in a decision-making process. However, because of randomness in the operation of memristors, variation is inevitable, affecting the accuracy of the computation and the trustworthiness of the system-based decisions. This work aims to combine novel 2-D materials with new array configurations to mitigate the impact of variability in order to improve accuracy and performance of computation, while maintaining the benefits of low-energy consumption. The intellectual significance of this project extends from fundamental science relating to atomic-scale storage in 2-D materials, to applied engineering contributions relating to the demonstration of robust memristor arrays for neuro-inspired computing applications.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.

模拟大脑的新型计算系统受到了科学界和工程界的极大关注，因为它们可以帮助分析大量信息，同时只消耗少量能量。这些神经启发系统已经取得了重大进展，但仍然存在一些限制，阻碍了它们在便携式设备上的实际应用。这项工作将研究新的材料和设计，可以缓解这些限制，使关键的数学函数能够更好地执行。这项工作产生的科学突破将支持数据密集型机器学习应用程序的健壮、高效和大规模并行计算。为支持未来认知系统和人工智能的新型计算硬件奠定基础，将产生深远的社会影响。该项目的另一个目标是通过培训学生(包括研究生和本科生研究人员)，通过开发基于网络的互动平台形式的教育工具，以及通过支持一门研究生水平的课程来整合研究和教育，该课程的主题是新颖的集成记忆和神经启发的计算体系结构。该项目将建立基于二维原子薄材料的新存储技术的基础，扩展神经启发系统的实际工程限制。这些被称为忆阻器的存储设备可以根据外部施加的刺激被编程为不同的状态。当按特定配置组织时，忆阻器阵列可用于实现系统在决策过程中使用的复杂功能。然而，由于忆阻器运行的随机性，其变化不可避免，影响了计算的准确性和系统决策的可信度。这项工作旨在将新的二维材料与新的阵列配置相结合，以减轻变异性的影响，以提高计算的精度和性能，同时保持低能耗的好处。该项目的知识意义从与2-D材料中原子尺度存储相关的基础科学，到与展示用于神经启发的计算应用的坚固记忆阻器阵列有关的应用工程贡献。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

The viability of analog-based accelerators for neuromorphic computing: a survey

• DOI: 10.1088/2634-4386/ac0242
• 发表时间: 2021-05
• 期刊: Neuromorphic Computing and Engineering
• 作者: Mirembe Musisi-Nkambwe;Sahra Afshari;H. Barnaby;M. Kozicki;Ivan Sanchez Esqueda

Hexagonal boron nitride (h-BN) memristor arrays for analog-based machine learning hardware

• DOI: 10.1038/s41699-022-00328-2
• 发表时间: 2022-07
• 期刊: npj 2D Materials and Applications
• 影响因子: 9.7
• 作者: Jing Xie;Sahra Afshari;Ivan Sanchez Esqueda

Dot-product computation and logistic regression with 2D hexagonal-boron nitride (h-BN) memristor arrays

• DOI: 10.1088/2053-1583/acdfe1
• 发表时间: 2023-06
• 期刊: 2D Materials
• 影响因子: 5.5
• 作者: Sahra Afshari;S. Radhakrishnan;Jing Xie;Mirembe Musisi-Nkambwe;Jian Meng;Wangxin He;J.-s. Seo