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http://dx.doi.org/10.7471/ikeee.2017.21.3.244

Pedestrian Inference Convolution Neural Network Using GP-GPU  

Jeong, Junmo (Dept. of Electronics Engineering, Seokyeong University)
Publication Information
Journal of IKEEE / v.21, no.3, 2017 , pp. 244-247 More about this Journal
Abstract
In this paper, we implemented a convolution neural network using GP-GPU. After defining the structure, CNN performed inferencing using the GP-GPU with 256 threads, which was the previous study, using the weight obtained from the training. Training used Intel i7-4470 CPU and Matlab. Dataset used Daimler Pedestrian Dataset. The GP-GPU is controlled by the PC using PCIe and operates as an FPGA. We assigned a thread according to the depth and size of each layer. In the case of the pooling layer, we used over warpping pooling to perform additional operations on the horizontal and vertical regions. One inferencing takes about 12 ms.
Keywords
Pedestrian Inference; CNN; GP-GPU; Multithread; SIMT;
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