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Efficient Brain-Inspired Hyperdimensional Learning with Spatiotemporal Structured Data

Brain-inspired hyperdimensional (HD) computing is a new computing paradigm based on theoretical neuroscience to enable efficient learning. In HD computing, the original data are encoded to points in a high-dimensional space to perform learning with lightweight algebra. In this paper, we propose STEMHD that elicits key features from spatiotemporal data along with a hardware design that empowers computation reuse. Our evaluation shows that STEMHD successfully interprets structural data at a low cost achieving higher accuracy than the state-of-the-art methods. Our evaluation shows that STEMHD improves performance and energy efficiency during the model training by 16.3% and 19.7%, respectively, with a negligible accuracy loss of less than 0.25%. For the model inference, we observe the inference speedup of 1.96× on average.