MicroHD: An Accuracy-Driven Optimization of Hyperdimensional Computing Algorithms for TinyML systems

TL;DR

MicroHD is a novel optimization method for hyperdimensional computing that iteratively tunes hyperparameters to minimize resource use while maintaining user-defined accuracy levels, enabling highly efficient TinyML applications.

Contribution

It introduces an accuracy-driven hyperparameter tuning approach for HDC that balances resource reduction with accuracy preservation, addressing limitations of previous methods.

Findings

Achieves up to 200x compression and efficiency gains.

Maintains accuracy degradation below 1%.

Demonstrates scalability for larger workloads.

Abstract

Hyperdimensional computing (HDC) is emerging as a promising AI approach that can effectively target TinyML applications thanks to its lightweight computing and memory requirements. Previous works on HDC showed that limiting the standard 10k dimensions of the hyperdimensional space to much lower values is possible, reducing even more HDC resource requirements. Similarly, other studies demonstrated that binary values can be used as elements of the generated hypervectors, leading to significant efficiency gains at the cost of some degree of accuracy degradation. Nevertheless, current optimization attempts do not concurrently co-optimize HDC hyper-parameters, and accuracy degradation is not directly controlled, resulting in sub-optimal HDC models providing several applications with unacceptable output qualities. In this work, we propose MicroHD, a novel accuracy-driven HDC optimization approach that iteratively tunes HDC hyper-parameters, reducing memory and computing requirements while ensuring user-defined accuracy levels. The proposed method can be applied to HDC implementations using different encoding functions, demonstrates good scalability for larger HDC workloads, and achieves compression and efficiency gains up to 200x when compared to baseline implementations for accuracy degradations lower than 1%.