ReLU-KAN: New Kolmogorov-Arnold Networks that Only Need Matrix Addition, Dot Multiplication, and ReLU
<|reference_start|>ReLU-KAN: New Kolmogorov-Arnold Networks that Only Need Matrix Addition, Dot Multiplication, and ReLU: Limited by the complexity of basis function (B-spline) calculations, Kolmogorov-Arnold Networks (KAN) suffer from restricted parallel computing capability on GPUs. This paper proposes a novel ReLU-KAN implementation that inherits the core idea of KAN. By adopting ReLU (Rectified Linear Unit) and point-wise multiplication, we simplify the design of KAN's basis function and optimize the computation process for efficient CUDA computing. The proposed ReLU-KAN architecture can be readily implemented on existing deep learning frameworks (e.g., PyTorch) for both inference and training. Experimental results demonstrate that ReLU-KAN achieves a 20x speedup compared to traditional KAN with 4-layer networks. Furthermore, ReLU-KAN exhibits a more stable training process with superior fitting ability while preserving the "catastrophic forgetting avoidance" property of KAN. You can get the code in https://github.com/quiqi/relu_kan<|reference_end|>
@article{qiu2024relu-kan:,
    title={ReLU-KAN: New Kolmogorov-Arnold Networks that Only Need Matrix Addition,
  Dot Multiplication, and ReLU},
    author={Qi Qiu, Tao Zhu, Helin Gong, Liming Chen, Huansheng Ning},
    journal={arXiv preprint arXiv:2406.02075},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.02075},
    primaryClass={cs.LG cs.NE}
}