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http://dx.doi.org/10.7471/ikeee.2017.21.4.420

Implementation of Neural Network Accelerator for Rendering Noise Reduction  

Nam, Kihun (Dept. of Computer Engineering, Seokyeong University)
Publication Information
Journal of IKEEE / v.21, no.4, 2017 , pp. 420-423 More about this Journal
Abstract
In this paper, we propose an implementation of a neural network accelerator for reducing the rendering noise. Among the rendering algorithms, we selects a ray tracing to assure a high-definition graphics. Ray tracing rendering uses ray to render. Less use of the ray will result in noise, and if used too much, it will produce a higher quality image, but will take longer. In order to quickly process such lace rendering, an algorithm is used that uses less rays and removes the noise generated. Among such algorithms, there is an algorithm using a neural network, and a neural network accelerator which obtains a filter parameter used in an operation is implemented in order to speed up the operation speed. The time it takes to calculate the parameters used for a pixel is 11.44us.
Keywords
Rendering; Neural network; MLP; noise; rays;
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