Aiming at the problems that it is difficult to improve performance and reduce power consumption in the process of High Level Synthesis (HLS), an automatic custom instructions identification method for high level synthesis was proposed. The enumeration and selection of custom instructions were implemented before high level synthesis, so as to provide a universal automatic custom instructions identification method for high level synthesis. Firstly, the high level source code was transformed into a Control Data Flow Graph (CDFG), and the source code was preprocessed. Secondly, a subgraph enumeration algorithm was used to enumerate all the connected convex subgraphs in a bottom-up manner from the Data Flow Graph (DFG) based on control data flow graph, which effectively improved the user's ability to flexibly modify the constraints. Then, considering the area, performance and code size, the subgraph selection algorithms were used to select partial optimal subgraphs as the final custom instructions. Finally, a new code was regenerated by incorporating the selected custom instructions as the input of high level synthesis. Compared with the traditional high level synthesis, the pattern selection based on frequency of occurrence reduced the area by an average of 19.1%. Meanwhile, the subgraph selection based on critical paths reduced the latency by an average of 22.3%. In addition, compared with Transitive Digraph (TD) algorithm, the enumeration efficiency of the proposed algorithm was increased by an average of 70.8%. The experimental results show that the automatic custom instructions identification method can significantly improve performance and reduce area and code size for high level synthesis in circuit design.

In view of the friend recommendation in Mobile Social Network (MSN), a new method based on multi-dimensional similarity was proposed. The method is a kind of method based on content, but not confined to single dimension matching information, it judges users' similarity of various dimensions from three aspects of space, time and interest, then gets a comprehensive judgment by "difference distance". The proposed method can recommend other users to target audience when they are consistent in geographical position, online-time and interest. The experimental results show that when the method is used in the friend recommendation of mobile social networks, its precision and efficiency are up to 80% and 60% respectively, the performance is much better than the other friend recommendation methods based on single dimension; at the same time, by adjusting the value of three dimensional weights, the method can be used in a variety of mobile social networks with different characteristics.