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

arXiv:2404.00039v1 Announce Type: cross
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 …

abstract accuracy algorithms applications arxiv computing cs.ai cs.lg cs.ne cs.pf dimensions math.oc memory optimization requirements space standard systems tinyml type values