A4 Article in conference proceedings
High-dimensional computing with sparse vectors

List of Authors: Mika Laiho, Jussi Poikonen, Pentti Kanerva, Eero Lehtonen
Publication year: 2015
Book title *: 2015 IEEE Biomedical Circuits and Systems Conference (BioCAS
Number of pages: 4
ISBN: 978-1-4799-7234-0
ISSN: 2163-4025


Computing with high-dimensional vectors in a manner that resembles computing with numbers is based on Plate's Holographic Reduced Representation (HRR) and is used to model human cognition. Here we examine its hardware realization under constraints suggested by the properties of the brain's circuits. The sparseness of neural firing suggests that the vectors should be sparse. We show that the HRR operations of addition, multiplication, and permutation can be realized with sparse vectors, making an energy-efficient implementation possible. Furthermore, we propose a processor that has both data and instructions embedded in the same high-dimensional vector. The operation is highlighted with a sequence memory example.

Last updated on 2019-20-07 at 08:53