In order to improve the accuracy of image classification with convolution network model without increasing more computation, a new image deep convolution classification method based on complex network description was proposed. Firstly, the complex network model degree matrices under different thresholds were obtained by using complex network description of image. Then, the feature vector was obtained by deep convolution neural networks based on degree matrix description of image. Finally, the obtained feature vectors were used for image K-Nearest Neighbors ( KNN) classification. The verification experiments were carried out on the ImageNet Large Scale Visual Recognition Challenge 2014 (ILSVRC2014) database. The experimental results show that the proposed model has higher accuracy and fewer iterations.

The Instruction Level Parallelism (ILP) of VLIW DSP processor is acquired through operation partitioning and software pipeline. In the previous research of operation partitioning, people always focus on reducing move operations between clusters, but rarely consider the effect of heterogeneous architecture and some registers that should be placed on reserved cluster. A method based on DataFlow Graph (DFG) for heterogeneous architecture was described to solve this problem. First, the DFG was partitioned into several sub-graphs according to the relations between operations, then the sub-graphs were refined with a heuristic method to meet the requirements of special registers. The experimental results show that this method can make the load of cluster more balanced, and achieve an average of 8% improvement over traditional method.