4 research outputs found
High Performance Depthwise and Pointwise Convolutions on Mobile Devices
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 5.5x and 2.1x against TVM (Chen et al. 2018) on DWConv and PWConv.Comment: 8 pages, Thirty-Four AAAI conference on Artificial Intelligenc
XXIII Edici贸n del Workshop de Investigadores en Ciencias de la Computaci贸n : Libro de actas
Compilaci贸n de las ponencias presentadas en el XXIII Workshop de Investigadores en Ciencias de la Computaci贸n (WICC), llevado a cabo en Chilecito (La Rioja) en abril de 2021.Red de Universidades con Carreras en Inform谩tic