Learning from Hypervectors: A Survey on Hypervector Encoding
Sercan Aygun Mehran Shoushtari Moghadam M. Hassan Najafi Mohsen Imani
Hyperdimensional computing (HDC) is an emerging computing paradigm that
imitates the brain's structure to offer a powerful and efficient processing and
learning model. In HDC, the data are encoded with long vectors, called
hypervectors, typically with a length of 1K to 10K. The literature provides
several encoding techniques to generate orthogonal or correlated hypervectors,
depending on the intended application. The existing surveys in the literature
often focus on the overall aspects of HDC systems, including system inputs,
primary computations, and final outputs. However, this study takes a more
specific approach. It zeroes in on the HDC system input and the generation of
hypervectors, directly influencing the hypervector encoding process. This
survey brings together various methods for hypervector generation from
different studies and explores the limitations, challenges, and potential
benefits they entail. Through a comprehensive exploration of this survey,
readers will acquire a profound understanding of various encoding types in HDC
and gain insights into the intricate process of hypervector generation for
diverse applications.