Collaborative Research: MLWiNS: Hyperdimensional Computing for Scalable IoT Intelligence Beyond the Edge

协作研究：MLWiNS：用于超越边缘的可扩展物联网智能的超维计算

基本信息

批准号：
2003279
负责人：
Tajana Rosing
金额：
$ 14万
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：
2020
资助国家：
美国
起止时间：
2020-07-01 至 2023-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2003279&HistoricalAwards=false
关键词：
Collaborative Research MLWiNS Hyperdimensional Computing

项目摘要

The Internet of Things (IoT) generates large amounts of data that machine learning algorithms today process in the cloud. The heterogeneity of the data types and devices, along with limited computing and communication capabilities of IoT devices, poses a significant challenge to real-time training and learning with classical machine learning algorithms. This project instead proposes to use Hyperdimensional (HD) computing for distributed machine learning. HD computing is a brain-inspired machine learning paradigm that transforms data into knowledge at very low cost, while being extremely robust to errors. When completed, this project has the potential to change the way machine learning is done today – instead of depending on the cloud, IoT systems will be able to make quality decisions on the spot, in real time, regardless of connectivity, with long battery lifetime. This will be made possible by designing: i) novel HD encoding schemes to represent various data in IoT applications including numerical feature vectors, time-series data, and images, ii) a novel distributed learning framework for IoT networks by incorporating active learning to considerably reduce communication overhead and learning costs, and iii) a reliable learning solution based on the error-tolerant characteristic of HD computing. The ideas developed in this project will be tested on both UCSD and SDSU using a fully instrumented testbed for human activity recognition.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.

物联网（IoT）生成了大量的数据，这些数据是在PES和设备中进行的，以及IoT设备的计算和通信能力能力有限经典的机器学习算法。，无论连接如何，都可以通过设计来实时。积极的学习以减少高清计算的沟通开销和学习成本。值得使用基金会的UAL功绩和更广泛影响的评论标准来支持Thalugh评估。

项目成果

期刊论文数量（18）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Towards a Robust and Efficient Classifier for Real World Radio Signal Modulation Classification

DOI：
10.1109/icassp49357.2023.10094907
发表时间：
2023-06
期刊：
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
影响因子：
0
作者：
Dancheng Liu;Kazim Ergun;Tajana Simunic
通讯作者：
Dancheng Liu;Kazim Ergun;Tajana Simunic

Automating and Optimizing Reliability-Driven Deployment in Energy-Harvesting IoT Networks

自动化和优化能量收集物联网网络中可靠性驱动的部署

DOI：
10.1109/tnsm.2022.3208083
发表时间：
2023
期刊：
IEEE Transactions on Network and Service Management
影响因子：
5.3
作者：
Yu, Xiaofan;Ergun, Kazim;Song, Xueyang;Cherkasova, Ludmila;Rosing, Tajana Šimunić
通讯作者：
Rosing, Tajana Šimunić

Multi-Label Classification With Hyperdimensional Representations

DOI：
10.1109/access.2023.3299881
发表时间：
2023
期刊：
IEEE Access
影响因子：
3.9
作者：
Rishikanth Chandrasekaran;Fatemeh Asgareinjad;Justine Morris;Tajana Rosing
通讯作者：
Rishikanth Chandrasekaran;Fatemeh Asgareinjad;Justine Morris;Tajana Rosing

HD-I-IoT: Hyperdimensional Computing for Resilient Industrial Internet of Things Analytics

DOI：
10.23919/date56975.2023.10137045
发表时间：
2023-04
期刊：
2023 Design, Automation & Test in Europe Conference & Exhibition (DATE)
影响因子：
0
作者：
Onat Güngör;Tajana Simunic;Baris Aksanli
通讯作者：
Onat Güngör;Tajana Simunic;Baris Aksanli

Poster Abstract: Attentive Multimodal Learning on Sensor Data using Hyperdimensional Computing

海报摘要：使用超维计算对传感器数据进行多模态学习

DOI：
10.1145/3583120.3589824
发表时间：
2023
期刊：
Proceedings of the 22nd International Conference on Information Processing in Sensor Networks
影响因子：
0
作者：
Zhao, Quanling;Yu, Xiaofan;Rosing, Tajana
通讯作者：
Rosing, Tajana