A Linear-Time, Optimization-Free, and Edge Device-Compatible Hypervector Encoding

Hyperdimensional computing (HDC) offers a single-pass learning system by imitating the brain-like signal structure. HDC data structure is in random hypervector format for better orthogonality. Similarly, in bit-stream processing - aka stochastic computing- systems, low-discrepancy (LD) sequences are used for the efficient generation of uncorrelated bit-streams. However, LD-based hypervector generation has never been investigated before. This work studies the utilization of LD Sobol sequences as a promising alternative for encoding hypervectors. The new encoding technique achieves highly-accurate classification with a single-time training step without needing to iterate repeatedly over random rounds. The accuracy evaluations in an embedded environment exhibit a classification rate improvement of up to 9. 79% compared to the conventional random hypervector encoding.