SHF:Small:Neuromorphic Architectures for On-line Learning

SHF:Small：用于在线学习的神经形态架构

• 批准号: 1718633
• 负责人: Tarek Taha
• 金额: $ 44万
• 依托单位: University of Dayton
• 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: 2017-08-15 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1718633&HistoricalAwards=false
• 关键词: SHF; Small; Neuromorphic; Architectures; line

With the increasingly large volumes of data being generated in all fields, it is difficult to draw meaningful understanding from the information. Deep learning is a collection of new algorithms that have been developed recently to make it easier to understand large volumes of data. These algorithms typically have two phases of operation: training and inference. In the training phase, the algorithms learn how to interpret data, while in the inference phase the trained algorithms process new data based on what they learned earlier. Training generally requires high power computing. This project will develop novel computing systems for training that require low power consumption. This makes them suitable for portable systems, and hence could enable the design of significantly smarter products that learn continuously from their environment and are able to better interact with the environment. The proposed work includes outreach to K-12 students and also training of undergraduate, graduate, and minority students. The novel computing systems to be developed will employ memristor circuits to accelerate the training phase of deep learning algorithms. Memristors are nanoscale resistive memory devices. The PIs will develop and characterize the memristors and then design deep learning circuits for training based on the characterized memristor devices. The PIs will also design computing systems based on the training circuits to be developed. These computing systems will have applications in a broad range of fields, including low power consumer products and high power clusters of computers.

随着各个领域产生的数据量越来越大，很难从这些信息中得出有意义的理解。深度学习是最近开发的新算法的集合，可以更容易地理解大量数据。这些算法通常有两个操作阶段：训练和推理。在训练阶段，算法学习如何解释数据，而在推理阶段，训练后的算法根据之前所学的内容处理新数据。训练通常需要高功率计算。该项目将为训练开发新的低功耗计算系统。这使得它们适用于便携式系统，因此可以设计出更智能的产品，这些产品可以不断地从环境中学习，并能够更好地与环境交互。拟议的工作包括向K-12学生提供服务，以及对本科生、研究生和少数民族学生的培训。即将开发的新型计算系统将采用忆阻电路来加速深度学习算法的训练阶段。忆阻器是纳米级阻性记忆器件。pi将开发和表征忆阻器，然后基于表征的忆阻器器件设计用于训练的深度学习电路。pi还将设计基于待开发的训练电路的计算系统。这些计算系统将在广泛的领域得到应用，包括低功耗消费产品和高功耗计算机集群。

项目成果

Transmission Electron Microscopy Study on the Effect of Thermal and Electrical Stimuli on Ge2Te3 Based Memristor Devices

• DOI: 10.3389/felec.2022.872163
• 发表时间: 2022-04
• 期刊: Chemistry of Materials
• 影响因子: 8.6
• 作者: Austin Shallcross;K. Mahalingam;E. Shin;G. Subramanyam;Md. Shahanur Alam;Tarek Taha;S. Ganguli;Cynthia T. Bowers;Benson Athey;A. Hilton;Anisha Roy;R. Dhall

Memristor Based Neuromorphic Adaptive Resonance Theory for One-Shot Online Learning and Network Intrusion Detection

• DOI: 10.1145/3407197.3407608
• 发表时间: 2020-07
• 期刊: International Conference on Neuromorphic Systems 2020
• 作者: Md. Shahanur Alam;C. Yakopcic;G. Subramanyam;T. Taha

Conversion of an Unsupervised Anomaly Detection System to Spiking Neural Network for Car Hacking Identification

将无监督异常检测系统转换为尖峰神经网络以进行汽车黑客识别

• DOI: 10.1109/igsc51522.2020.9291232
• 发表时间: 2020
• 期刊: International Green and Sustainable Computing Workshops (IGSC
• 作者: Jaoudi, Yassine;Yakopcic, Chris;Taha, Tarek
• 通讯作者: Taha, Tarek

Memristor Based Neuromorphic Network Security System Capable of Online Incremental Learning and Anomaly Detection

基于忆阻器的神经形态网络安全系统，能够在线增量学习和异常检测

• 发表时间: 2020
• 期刊: International Green and Sustainable Computing Conference (IGSC
• 作者: Alam, Md. Shahanur;Yakopcic, Chris;Subramanyam, Guru;Taha, Tarek M.
• 通讯作者: Taha, Tarek M.

Design Space Evaluation of a Memristor Crossbar Based Multilayer Perceptron for Image Processing

• DOI: 10.1109/ijcnn.2019.8852005
• 发表时间: 2019-07
• 期刊: 2019 International Joint Conference on Neural Networks (IJCNN)
• 作者: C. Yakopcic;B. R. Fernando;T. Taha