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

物联网(IoT)产生大量数据，今天的机器学习算法在云中处理这些数据。数据类型和设备的异构性，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。相反，该项目建议使用超维(HD)计算来进行分布式机器学习。高清计算是一种受大脑启发的机器学习范例，它以非常低的成本将数据转换为知识，同时对错误具有极强的健壮性。该项目完成后，有可能改变当今机器学习的方式--不再依赖云，物联网系统将能够在现场实时做出高质量的决策，无论连接如何，电池寿命长。这将通过设计：i)新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著降低通信开销和学习成本，为物联网网络设计新的分布式学习框架，以及iii)基于高清计算的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加州大学圣地亚哥分校和圣地亚哥州立大学使用全仪器人类活动识别试验台进行测试。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.