07 Jun 2026

This post is a high-level explainer for my Master’s thesis, which involves designing hardware architectures for ultrafast inference and online learning using the Kolmogorov-Arnold Network (KAN) architecture. I’ll assume familiarity with standard machine learning concepts, as well as some understanding of hardware and digital circuits; read my previous post here for the latter.

Please read the two papers below for more information, particularly for details on benchmarks and notable results.

[FPGA 2026 Best Paper]

Duc Hoang * , Aarush Gupta * , and Philip C. Harris. “KANELÉ: Kolmogorov–Arnold Networks for Efficient LUT-based Evaluation.” Proceedings of the 2026 ACM/SIGDA International Symposium on Field Programmable Gate Arrays . ACM, 2026. https://dx.doi.org/10.1145/ 3748173.3779202

[ICML 2026]

Duc Hoang * , Aarush Gupta * , and Philip Harris. “Ultrafast on-FPGA Online Learning via Spline Locality in Kolmogorov-Arnold Networks.” arXiv preprint arXiv:2602.02056 , 2026.…