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）会产生大量数据，如今机器学习算法会在云端处理这些数据。数据类型和设备的异质性，以及物联网设备有限的计算和通信能力，对使用经典机器学习算法进行实时训练和学习构成了重大挑战。该项目建议使用超高维（HD）计算进行分布式机器学习。高清计算是一种以大脑为灵感的机器学习范式，它以极低的成本将数据转化为知识，同时对错误具有极强的鲁棒性。完成后，该项目有可能改变当今机器学习的方式，而不是依赖于云，物联网系统将能够在现场实时做出高质量的决策，无论连接情况如何，电池寿命都很长。这将通过设计：i)新颖的高清编码方案来表示物联网应用中的各种数据，包括数字特征向量，时间序列数据和图像，ii)通过结合主动学习来显着降低通信开销和学习成本的物联网网络的新型分布式学习框架，以及iii)基于高清计算容错特性的可靠学习解决方案。在这个项目中开发的想法将在UCSD和SDSU进行测试，使用一个完全仪器化的人类活动识别测试平台。该奖项反映了美国国家科学基金会的法定使命，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

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.

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

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.