An Extension to Basis-Hypervectors for Learning from Circular Data in Hyperdimensional Computing

Hyperdimensional Computing (HDC) is a computation framework based on random vector spaces, particularly useful for machine learning in resource-constrained environments. The encoding of information to the hyperspace is the most important stage in HDC. At its heart are basis-hypervectors, responsible...

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Bibliographic Details
Published in:2023 60th ACM/IEEE Design Automation Conference (DAC) pp. 1 - 6
Main Authors: Nunes, Igor, Heddes, Mike, Givargis, Tony, Nicolau, Alexandru
Format: Conference Proceeding
Language:English
Published: IEEE 09.07.2023
Subjects:
basis-hypervectors
circular data
Computational modeling
Data models
Design automation
Encoding
Heart
hyperdimensional computing
information theory
Machine learning
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Summary:Hyperdimensional Computing (HDC) is a computation framework based on random vector spaces, particularly useful for machine learning in resource-constrained environments. The encoding of information to the hyperspace is the most important stage in HDC. At its heart are basis-hypervectors, responsible for representing atomic information. We present a detailed study on basis-hypervectors, leading to broad contributions to HDC: 1) an improvement for level-hypervectors, used to encode real numbers; 2) a method to learn from circular data, an important type of information never before addressed in HDC. Results indicate that these contributions lead to considerably more accurate models for classification and regression.
DOI:10.1109/DAC56929.2023.10247736