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Open CUDA convolution neural network inference implementation
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DOI：10.1007/s10586-025-05895-9.png)
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Abstract
En 中文
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.
Keywords:
CNN
CUDA
GPU
Convolution
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