  Kolmogorov-Arnold Networks (KANs), a novel type of neural network, have
recently gained popularity and attention due to the ability to substitute
multi-layer perceptions (MLPs) in artificial intelligence (AI) with higher
accuracy and interoperability. However, KAN assessment is still limited and
cannot provide an in-depth analysis of a specific domain. Furthermore, no study
has been conducted on the implementation of KANs in hardware design, which
would directly demonstrate whether KANs are truly superior to MLPs in practical
applications. As a result, in this paper, we focus on verifying KANs for
classification issues, which are a common but significant topic in AI using
four different types of datasets. Furthermore, the corresponding hardware
implementation is considered using the Vitis high-level synthesis (HLS) tool.
To the best of our knowledge, this is the first article to implement hardware
for KAN. The results indicate that KANs cannot achieve more accuracy than MLPs
in high complex datasets while utilizing substantially higher hardware
resources. Therefore, MLP remains an effective approach for achieving accuracy
and efficiency in software and hardware implementation.
