Ultrafast FPGA-based inference and online learning using Kolmogorov-Arnold Networks
By
Aarush Gupta
The kind of bagel that ruins lesser bagels for you.
Summary
This post explains the author's Master's thesis on designing hardware architectures for ultrafast inference and online learning using Kolmogorov-Arnold Networks (KAN) on FPGAs. The work won the FPGA 2026 Best Paper award. The author assumes familiarity with ML concepts and digital circuits, and references two papers for detailed benchmarks and results.
Key quotes
· 3 pulledThis 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.
[FPGA 2026 Best Paper] Duc Hoang*, Aarush Gupta*, and Phi
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