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

• 批准号: 2003277
• 负责人: Baris Aksanli
• 金额: $ 6万
• 依托单位: San Diego State University Foundation
• 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=2003277&HistoricalAwards=false
• 关键词: Collaborative; Research; MLWiNS; Hyperdimensional; Computing

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

Multi-label HD Classification in 3D Flash

• DOI: 10.1109/vlsi-soc46417.2020.9344070
• 发表时间: 2020-10
• 期刊: 2020 IFIP/IEEE 28th International Conference on Very Large Scale Integration (VLSI-SOC)
• 作者: Justin Morris;Yilun Hao;Saransh Gupta;R. Ramkumar;Jeffrey Yu;M. Imani;Baris Aksanli;Tajana Simunic

HD2FPGA: Automated Framework for Accelerating Hyperdimensional Computing on FPGAs

HD2FPGA：加速 FPGA 上超维计算的自动化框架

• DOI: 10.1109/isqed57927.2023.10129332
• 发表时间: 2023
• 期刊: 2023 24th International Symposium on Quality Electronic Design (ISQED
• 作者: Zhang, Tinaqi;Salamat, Sahand;Khaleghi, Behnam;Morris, Justin;Aksanli, Baris;Rosing, Tajana Simunic
• 通讯作者: Rosing, Tajana Simunic

AdaptBit-HD: Adaptive Model Bitwidth for Hyperdimensional Computing

AdaptBit-HD：超维计算的自适应模型位宽

• DOI: 10.1109/iccd53106.2021.00026
• 发表时间: 2021
• 期刊: The 39th IEEE International Conference on Computer Design
• 作者: Morris, J.

HyDREA: Towards More Robust and Efficient Machine Learning Systems with Hyperdimensional Computing

HyDREA：通过超维计算实现更强大、更高效的机器学习系统

• DOI: 10.23919/date51398.2021.9474218
• 发表时间: 2021
• 期刊: Automation & Test in Europe Conference & Exhibition (DATE
• 作者: Morris, J.

Adversarial-HD: Hyperdimensional Computing Adversarial Attack Design for Secure Industrial Internet of Things

• DOI: 10.1145/3576914.3587484
• 发表时间: 2023-05
• 期刊: Proceedings of Cyber-Physical Systems and Internet of Things Week 2023
• 作者: Onat Gungor;T. Rosing;Baris Aksanli