An Overview of graph optimization methods based on OpenVX
Convolutional neural networks have achieved great success in the field of image recognition,deep learning and convolutional neural networks have become research hotspots.Neural network models are deployed by high-performance heterogeneous architecture chips.To achieve high-performance computing on heterogeneous platforms,an OpenVX execution model graph-based is provided.Graph optimization technology make the hardware platform execute graph more efficiently.This paper introduces the OpenVX programming framework,then it focuses on the graph optimization technology from five aspects:node fusion,node transformation,node deletion,node splitting and node swap.Finally,the development trend of graph optimization technology is pointed out.
NETL
NSTL
万方数据
维普
