  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%.
