Presentation
SMORE: Similarity-Based Hyperdimensional Domain Adaptation for Multi-Sensor Time Series Classification
DescriptionMany real-world applications of the Internet of Things (IoT) employ machine learning (ML) algorithms to analyze time series information collected by interconnected sensors. However, distribution shift, a fundamental challenge in data-driven ML, arises when a model is deployed on a data distribution different from the training data and can substantially degrade model performance. Additionally, increasingly sophisticated deep neural networks (DNNs) are proposed to capture intricate spatial and temporal dependencies in multi-sensor time series data, often exceeding the capabilities of today's edge devices. In this paper, we propose SMORE, a novel resource-efficient domain adaptation (DA) algorithm for multi-sensor time series classification, leveraging the ultra-efficient operations of hyperdimensional computing. SMORE dynamically customizes test-time models with explicit consideration of the domain context of each sample to provide accurate predictions when confronted with domain shifts. Our evaluation on a variety of multi-sensor time series classification tasks shows that SMORE achieves on average 1.98% higher accuracy than state-of-the-art (SOTA) DNN-based DA algorithms with 18.81× faster training and 4.85× faster inference.
Event Type
Research Manuscript
TimeWednesday, June 2611:00am - 11:15am PDT
Location3001, 3rd Floor
AI
AI/ML Algorithms