[2D4-GS-2] Machine learning

[2D4-GS-2-04] Performance evaluation of GPU-based CNN for object detection on SoC system

〇Takuya Uesugi¹, Masato Gocho¹, Yuta Kawakami¹, Hiroshi Sakamaki¹ (1. Mitsubishi Electric Corporation)

Keywords:CNN, SoC, GPU

CNN (Convolutional Neural Network), which is used for image recognition and object detection and has recently been implemented in the System on Chip (SoC) environment, has a large amount of computation in the convolution layer, so performance may be degraded in the SoC environment. In this research, as an investigation of speeding up Convolution calculations for GPUs on SoC, an algorithm that solves Convolution operation by matrix multiplication was implemented with OpenCL, and the processing time of the object detection algorithm YOLO-Nano was measured on Intel and Qualcomm SoCs. As a result, compared to the Tensorflow Lite CPU, Qualcomm and Intel CPUs achieved speed improvement effects of 1.04 times and 6.37 times, respectively, and GPUs achieved speed improvement effects of 1.21 times and 1.52 times, respectively.