arXiv:2511.18689v3 Announce Type: replace Abstract: Kolmogorov--Arnold Networks (KANs) replace linear weights with spline-based functions, offering strong expressivity but posing challenges for low-precision deployment due to heterogeneous parameter distributions. We introduce QuantKAN, the first unified framework for quantization-aware training (QAT) and post-training quantization (PTQ) of KANs. The framework employs branch-aware quantizers for base and spline parameters and extends modern QAT and PTQ methods to spline-based layers across EfficientKAN, FastKAN, PyKAN, and KAGN. Experiments on
The rapid development and deployment of KANs necessitate solutions for efficient, low-precision implementation, which is often a bottleneck for real-world AI applications.
This framework addresses a significant challenge in deploying expressive Kolmogorov-Arnold Networks, potentially enabling wider adoption and more efficient AI inference across various hardware.
The ability to quantize KANs effectively removes a major barrier to their practical application, making them more competitive with traditional neural networks in resource-constrained environments.
- · AI hardware manufacturers
- · Edge AI developers
- · AI model deployers
- · Energy-efficient computing
- · High-precision AI inference-only solutions
More widespread deployment of Kolmogorov-Arnold Networks in production environments due to improved efficiency.
Increased competition and innovation in AI model architectures as KANs become more viable alternatives to MLPs.
Potential for new specialized hardware or software stacks optimized for spline-based network inference.
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Read at arXiv cs.LG