An improved calculation system for phase-functioned neural network and implementation in unreal engine
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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.
KeywordsComputing methodologies Procedural animation Artificial intelligence
This work is supported by Sichuan Sci-Tech Support Plan, Item Numbers: 2017GZ0025, 2017GZ0321 and 2016GZ0313.
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