Attentive Multimodal Learning on Sensor Data using Hyperdimensional Computing
PROCEEDINGS OF THE 2023 THE 22ND INTERNATIONAL CONFERENCE ON INFORMATION PROCESSING IN SENSOR NETWORKS, IPSN 2023(2023)
摘要
With the continuing advancement of ubiquitous computing and various sensor technologies, we are observing a massive population of multimodal sensors at the edge which posts significant challenges in fusing the data. In this poster we propose MultimodalHD, a novel Hyperdimensional Computing (HD)-based design for learning from multimodal data on edge devices. We use HD to encode raw sensory data to high-dimensional low-precision hypervectors, after which the multimodal hypervectors are fed to an attentive fusion module for learning richer representations via inter-modality attention. Our experiments on multimodal time-series datasets show MultimodalHD to be highly efficient. MultimodalHD achieves 17x and 14x speedup in training time per epoch on HAR and MHEALTH datasets when comparing with state-of-the-art RNNs, while maintaining comparable accuracy performance.
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关键词
Hyperdimensional Computing,Multimodal Learning
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