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.

翻译：Kolmogorov-Arnold网络（KANs）作为一种新型神经网络，近期因其能够以更高精度与可解释性替代人工智能（AI）中的多层感知机（MLPs）而受到广泛关注。然而，当前对KAN的评估仍显不足，未能针对特定领域展开深入分析。此外，尚未有研究探讨KAN在硬件设计中的实现方案，而这将直接验证KAN在实际应用中是否真正优于MLP。为此，本文聚焦于使用四类不同数据集验证KAN在AI领域常见且重要的分类问题上的性能，并进一步采用Vitis高层次综合（HLS）工具完成对应的硬件实现。据我们所知，这是首篇实现KAN硬件化的研究。实验结果表明：在高复杂度数据集上，KAN未能取得比MLP更高的精度，同时消耗了显著更多的硬件资源。因此，MLP在软件与硬件实现中仍是兼顾精度与效率的有效方案。