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<p>This work presents an open, efficient (fast) CUDA convolution neural network inference implementation specialized in some layers of popular nets like ResNet, VGG, and GoogLeNet. The proposed algorithm implements convolution directly instead of preprocessing with image to columns. Algorithm parameters are selected to meet constraints on global and shared memory access bandwidth, register usage, shared memory usage, and instructions per clock. Parallel arithmetic operations and memory access are achieved with several parallel blocks per streaming processor. Results comparing with state-of-the-art implementations are presented.</p>

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Open CUDA convolution neural network inference implementation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月9日

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  • Paulo Lopes

摘要

This work presents an open, efficient (fast) CUDA convolution neural network inference implementation specialized in some layers of popular nets like ResNet, VGG, and GoogLeNet. The proposed algorithm implements convolution directly instead of preprocessing with image to columns. Algorithm parameters are selected to meet constraints on global and shared memory access bandwidth, register usage, shared memory usage, and instructions per clock. Parallel arithmetic operations and memory access are achieved with several parallel blocks per streaming processor. Results comparing with state-of-the-art implementations are presented.