Article thumbnail

Classification using Hyperdimensional Computing: A Review

By Lulu Ge and Keshab K. Parhi

Abstract

Hyperdimensional (HD) computing is built upon its unique data type referred to as hypervectors. The dimension of these hypervectors is typically in the range of tens of thousands. Proposed to solve cognitive tasks, HD computing aims at calculating similarity among its data. Data transformation is realized by three operations, including addition, multiplication and permutation. Its ultra-wide data representation introduces redundancy against noise. Since information is evenly distributed over every bit of the hypervectors, HD computing is inherently robust. Additionally, due to the nature of those three operations, HD computing leads to fast learning ability, high energy efficiency and acceptable accuracy in learning and classification tasks. This paper introduces the background of HD computing, and reviews the data representation, data transformation, and similarity measurement. The orthogonality in high dimensions presents opportunities for flexible computing. To balance the tradeoff between accuracy and efficiency, strategies include but are not limited to encoding, retraining, binarization and hardware acceleration. Evaluations indicate that HD computing shows great potential in addressing problems using data in the form of letters, signals and images. HD computing especially shows significant promise to replace machine learning algorithms as a light-weight classifier in the field of internet of things (IoTs).Comment: IEEE Circuits and Systems Magazine (2020

Topics: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Neural and Evolutionary Computing, Electrical Engineering and Systems Science - Signal Processing
Publisher: 'Institute of Electrical and Electronics Engineers (IEEE)'
Year: 2020
DOI identifier: 10.1109/MCAS.2020.2988388
OAI identifier: oai:arXiv.org:2004.11204

Suggested articles


To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.