High Performance Depthwise and Pointwise Convolutions on Mobile Devices

Authors

  • Pengfei Zhang The Chinese University of Hong Kong
  • Eric Lo The Chinese University of Hong Kong
  • Baotong Lu The Chinese University of Hong Kong

DOI:

https://doi.org/10.1609/aaai.v34i04.6159

Abstract

Lightweight convolutional neural networks (e.g., MobileNets) are specifically designed to carry out inference directly on mobile devices. Among the various lightweight models, depthwise convolution (DWConv) and pointwise convolution (PWConv) are their key operations. In this paper, we observe that the existing implementations of DWConv and PWConv are not well utilizing the ARM processors in the mobile devices, and exhibit lots of cache misses under multi-core and poor data reuse at register level. We propose techniques to re-optimize the implementations of DWConv and PWConv based on ARM architecture. Experimental results show that our implementation can respectively achieve a speedup of up to 5.5× and 2.1× against TVM (Chen et al. 2018) on DWConv and PWConv.

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Published

2020-04-03

How to Cite

Zhang, P., Lo, E., & Lu, B. (2020). High Performance Depthwise and Pointwise Convolutions on Mobile Devices. Proceedings of the AAAI Conference on Artificial Intelligence, 34(04), 6795-6802. https://doi.org/10.1609/aaai.v34i04.6159

Issue

Vol. 34 No. 04: AAAI-20 Technical Tracks 4

Section

AAAI Technical Track: Machine Learning