Brain-inspired Hyperdimensional computing (HDC) emulates the human brain in the way of memorization, association, and reasoning. At its essence, HDC is about comparing and manipulating vectors of large sizes, making it a good candidate for the realm of AI. In the classification task, associative memory (AM) is responsible for finding the best match between classes hypervectors and query hypervector using Hamming distance (HM) approach. This paper proposes a digital design for the hyperdimensional associative memory (HDAM) main functionality. The DHAM architectures search for the nearest HM and can be scaled linearly to any vector dimension. The design space is explored for 2-options, namely sequential (SDHAM), optimized for the low area but has high latency, and parallel one (PDHAM), optimized for low latency but incur high area