Optimizing CNN Model Inference on CPUs

Authors: 

Yizhi Liu, Yao Wang, Ruofei Yu, Mu Li, Vin Sharma, and Yida Wang, Amazon

Abstract: 

The popularity of Convolutional Neural Network (CNN) models and the ubiquity of CPUs imply that better performance of CNN model inference on CPUs can deliver significant gain to a large number of users. To improve the performance of CNN inference on CPUs, current approaches like MXNet and Intel OpenVINO usually treat the model as a graph and use the high-performance libraries such as Intel MKL-DNN to implement the operations of the graph. While achieving reasonable performance on individual operations from the off-the-shelf libraries, this solution makes it inflexible to conduct optimizations at the graph level, as the local operation-level optimizations are predefined. Therefore, it is restrictive and misses the opportunity to optimize the end-to-end inference pipeline as a whole. This paper presents NeoCPU, a comprehensive approach of CNN model inference on CPUs that employs a full-stack and systematic scheme of optimizations. NeoCPU optimizes the operations as templates without relying on third-parties libraries, which enables further improvement of the performance via operation- and graph-level joint optimization. Experiments show that NeoCPU achieves up to 3.45× lower latency for CNN model inference than the current state-of-the-art implementations on various kinds of popular CPUs.

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BibTeX
@inproceedings {234946,
author = {Yizhi Liu and Yao Wang and Ruofei Yu and Mu Li and Vin Sharma and Yida Wang},
title = {Optimizing {CNN} Model Inference on CPUs},
booktitle = {2019 {USENIX} Annual Technical Conference ({USENIX} {ATC} 19)},
year = {2019},
isbn = {978-1-939133-03-8},
address = {Renton, WA},
pages = {1025--1040},
url = {https://www.usenix.org/conference/atc19/presentation/liu-yizhi},
publisher = {{USENIX} Association},
month = jul,
}

Presentation Video