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An improved calculation system for phase-functioned neural network and implementation in unreal engine

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Abstract

The problem attempting to be solved in this paper is optimizing phase-functioned neural network to support generated animation for game engine. The approach adopted is using CUDA and parallel programming to improve large prediction of matrices calculation. The results of this research included a 4-layer architecture of PFNN prediction framework, a CUDA calculation solution and a showcase binding in unreal engine. As for the effects of the results obtained, PFNN calculation has been sped up from 1.8 ms to 1.0, 1.1 ms. And according to the result of performance test of the utility of PFNN in real game development, its optimization has been proven.

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Acknowledgements

This work is supported by Sichuan Sci-Tech Support Plan, Item Numbers: 2017GZ0025, 2017GZ0321 and 2016GZ0313.

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Authors and Affiliations

  1. Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China

    Ping Kuang, Dingli Luo & Haoshuang Wang

  2. Chengdu Productivity Promotion Center, Chengdu, China

    Lixue Zhang

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  1. Ping Kuang
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  2. Dingli Luo
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  3. Haoshuang Wang
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  4. Lixue Zhang
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Correspondence to Haoshuang Wang.

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Kuang, P., Luo, D., Wang, H. et al. An improved calculation system for phase-functioned neural network and implementation in unreal engine. Cluster Comput 22 (Suppl 6), 15505–15516 (2019). https://doi.org/10.1007/s10586-018-2671-4

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