Abstract
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.
| Original language | English |
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| Title of host publication | Proceedings - 29th International Symposium on the Modeling, Analysis, and Simulation of Computer and Telecommunication Systems, MASCOTS 2021 |
| Publisher | IEEE Computer Society |
| ISBN (Electronic) | 9781665458382 |
| DOIs | |
| State | Published - 2021 |
| Event | 29th International Symposium on the Modeling, Analysis, and Simulation of Computer and Telecommunication Systems, MASCOTS 2021 - Houston, United States Duration: 3 Nov 2021 → 5 Nov 2021 |
Publication series
| Name | Proceedings - IEEE Computer Society's Annual International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems, MASCOTS |
|---|---|
| ISSN (Print) | 1526-7539 |
Conference
| Conference | 29th International Symposium on the Modeling, Analysis, and Simulation of Computer and Telecommunication Systems, MASCOTS 2021 |
|---|---|
| Country/Territory | United States |
| City | Houston |
| Period | 3/11/21 → 5/11/21 |
Bibliographical note
Publisher Copyright:© 2021 IEEE.
Keywords
- Alternative Computing
- Data representation
- Hyperdimensional Computing
