To address the problems of high memory access overhead， redundant computation， and limited efficient deployment caused by the large parameter size of Convolutional Neural Networks （CNNs）， a CNN pruning and quantization compression method for reconfigurable structures was proposed to integrate network structure characteristics and hardware deployment requirements， and to perform co-optimization from both pruning and quantization dimensions. First， a convolutional layer pruning strategy based on feature similarity was introduced， in which feature evaluation， cluster and grouping， similarity calculation， and redundancy removal were performed in turn to filter out low-contribution and redundant filters. Second， progressive threshold pruning was applied at the fully connected layers to compress redundant weights. Third， in the quantization part， layer sensitivity indices were constructed using Hessian traces， and the precision of each layer was assigned adaptively under a bit-width budget. Finally， combining the characteristics of reconfigurable structures， an optimized deployment scheme was designed. Experimental results on the CIFAR-10 dataset show that the proposed method achieves a compression ratio of 16.2x for VGG16， surpassing Automated deep neural network Pruning and Quantization framework （APQ） （13.9x）. Compared to the model using fixed 16-bit precision， the pruned VGG16 using the proposed deployment scheme on a self-reconfigurable and self-evolvable Artificial Intelligence （AI） chip has the inference latency reduced from 23.3 ms to 9.1 ms， achieving a 2.56x speedup. It can be seen that the proposed method reduces the storage and transmission costs while maintaining the classification accuracy， improving the deployment efficiency and computational performance on edge devices.

An index of network evolution speed and a network evolution model were put forward to analyze the effects of network evolution speed on propagation. The definition of temporal correlation coefficient was modified to characterize the speed of the network evolution; meanwhile, a non-Markov model of temporal networks was proposed. For every active node at a time step, a random node from network was selected with probability r, while a random node from former neighbors of the active node was selected with probability 1-r. Edges were created between the active node and its corresponding selected nodes. The simulation results confirm that there is a monotone increasing relationship between the network model parameter r and the network evolution speed; meanwhile, the greater the value of r, the greater the scope of the spread on network becomes. These mean that the temporal networks with high evolution speed are conducive to the spread on networks. More specifically, the rapidly changing network topology is conducive to the rapid spread of information, but not conducive to the suppression of virus propagation.