English

ILP-M Conv: Optimize Convolution Algorithm for Single-Image Convolution Neural Network Inference on Mobile GPUs

Distributed, Parallel, and Cluster Computing 2019-10-04 v2 Computer Vision and Pattern Recognition Performance

Abstract

Convolution neural networks are widely used for mobile applications. However, GPU convolution algorithms are designed for mini-batch neural network training, the single-image convolution neural network inference algorithm on mobile GPUs is not well-studied. After discussing the usage difference and examining the existing convolution algorithms, we proposed the HNTMP convolution algorithm. The HNTMP convolution algorithm achieves 14.6×14.6 \times speedup than the most popular \textit{im2col} convolution algorithm, and 2.30×2.30 \times speedup than the fastest existing convolution algorithm (direct convolution) as far as we know.

Keywords

Cite

@article{arxiv.1909.02765,
  title  = {ILP-M Conv: Optimize Convolution Algorithm for Single-Image Convolution Neural Network Inference on Mobile GPUs},
  author = {Zhuoran Ji},
  journal= {arXiv preprint arXiv:1909.02765},
  year   = {2019}
}
R2 v1 2026-06-23T11:07:29.606Z