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While separable convolutions have demonstrated great performance in network design, they suffer from poor efficiency on Tensor Cores-equipped GPUs. This paper proposes TensorFuse, which exploits the Tensor Cores by transforming nested-loops into hierarchy matrix multiplications for kernel fusion. TensorFuse minimizes redundant memory accesses by efficiently lowering GEMM-based convolution along execution hierarchy: from shared memory to register files. Compared with the state-of-the-art, it achieves up to 2.60 × inference speed on Tensor Cores. Furthermore, we explore the performance of network decoupling with multiple separable convolutions. TensorFuse consistently outperforms state-of-the-art libraries with up to 2.76 × acceleration on modern CNN benchmarks.

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Unlocking the Full Potential of Separable Convolutions on Tensor Cores

  • 会议论文

  • 发表日期：

    2025年

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  • Aodie Cui,
  • Chuangxin Zhao,
  • Xiaoyu Deng,
  • Gaozhe Jiang,
  • Yifan Yang,
  • Guangzhen Yao,
  • Renda Han,
  • Wenxin Zhang,
  • Xi Xuan

摘要

While separable convolutions have demonstrated great performance in network design, they suffer from poor efficiency on Tensor Cores-equipped GPUs. This paper proposes TensorFuse, which exploits the Tensor Cores by transforming nested-loops into hierarchy matrix multiplications for kernel fusion. TensorFuse minimizes redundant memory accesses by efficiently lowering GEMM-based convolution along execution hierarchy: from shared memory to register files. Compared with the state-of-the-art, it achieves up to 2.60 × inference speed on Tensor Cores. Furthermore, we explore the performance of network decoupling with multiple separable convolutions. TensorFuse consistently outperforms state-of-the-art libraries with up to 2.76 × acceleration on modern CNN benchmarks.