KANELÉ: Kolmogorov-Arnold Networks for Efficient LUT-based Evaluation — mentioned by ag2718

Wed, 10 Jun 2026 01:39:41 GMT

There is definitely a precision-performance tradeoff to consider. We explored this through ablation studies on bitwidth precision / resource usage in our work (Figure 6a in https://arxiv.org/pdf/2512.12850, Figure 4 in https://arxiv.org/pdf/2602.02056). Further exploration into the mechanics here would definitely be useful. Regarding your point that "90% of the benefit of KANs can be gained from a small variety of function shapes": even within the B-spline basis, the shapes are quite uniform. Much of the actual benefit of scaling up the basis size comes from learning more complex…

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KANELÉ: Kolmogorov-Arnold Networks for Efficient LUT-based Evaluation · HackerPapers discussions

KANELÉ: Kolmogorov-Arnold Networks for Efficient LUT-based Evaluation — mentioned by ag2718