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2020 AAAI AAAI 2020

High Performance Depthwise and Pointwise Convolutions on Mobile Devices

Abstract

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.

🌉 Interdisciplinary Bridge — Deep Learning and Machine Learning
🧭 Keyword Pioneer — arm processor
🐣 Hot Topic Early Bird — inference optimization
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Security & Privacy, Speech & Audio

Authors

Pengfei Zhang , Eric Lo , Baotong Lu

Topics

Machine Learning > Application Areas > Efficient Computing Deep Learning > Techniques > Model Architecture Machine Learning > Application Areas > Model Compression Deep Learning > Optimization & Theory > Efficient Computing Deep Learning > Architectures > Convolutional Neural Networks

Keywords

efficient computing inference optimization mobile device depthwise convolution arm processor pointwise convolution
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