2013, Vol.18, No.10

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

pISSN : 1598-849X / eISSN : 2383-9945
https://journal.kci.go.kr/jksci

This paper explores design space of many-core processors for a fire feature extraction algorithm. This paper evaluates the impact of varying the number of cores and memory sizes for themany-core processor and identifies an optimal many-core processor in terms of performance, energy efficiency, and area efficiency. In this study, we utilized 90 samples with dimensions of 256×256(60 samples containing fire and 30 samples containing non-fire) for experiments. Experimental results using six different many-core architectures (PEs=16, 64, 256, 1,024, 4,096, and 16,384)and the feature extraction algorithm of fire indicate that the highest area efficiency and energy efficiency are achieved at PEs=1,024 and 4,096, respectively, for all fire/non-fire containing movies. In addition, all the six many-core processors satisfy the real-time requirement of 30frames-per-second (30 fps) for the algorithm.

Recently, although the number of cores on a chip multi-processor increases, multi-programming or multi-threaded programming techniques to utilize the whole cores are still insufficient. Therefore, there inevitably exist some idle cores which are not working. This results in a waste of the caches, so-called idle caches which are dedicated to those idle cores. In this research, we propose amethodology to exploit idle caches effectively as victimcaches of on-chip memory resource. In simulation results, we have achieved 19.4%and 10.2%IPC improvement in 4-core and 16-core respectively, compared to previous technique.

In this paper, the proposed movie recommender system constructs trust network, which is similar to social network, using user's trust information that users explicitly present. Recommendation on items is performed by using relation degree between users and information of recommended item is provided by a visualization method. We discover the hidden relationships via the constructed trust network. To provide visualized recommendation information, we employ Freebase which is large knowledge base supporting information such as movie, music, and people in structured format. We provide three visualization methods as the followings: ⅰ) visualization based on movie posters with the number of movies that user required. ⅱ) visualization on extra information such as director, actor and genre and so on when user selected a movie from recommendation list. ⅲ) visualization based on movie posters that is recommended by neighbors who a user selects from trust network. The proposed system considers user's social relations and provides visualization which can reflect user's requirements. Using the visualization methods, user can reach right decision making on items. Furthermore, the proposed system reflects the user's opinion through recommendation visualization methods and can provide rich information to users through LOD(Linked Open Data) Cloud such as Freebase, LinkedMDB and Wikipedia and so on.