• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.11B
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• pp.1329-1338
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• 2011

This paper proposes an algorithm and hardware architecture for a broadcast communication which has the worst bottleneck among multiprocessor using distributed memory architectures. In conventional systems, collective communication is converted into point-to-point communications by MPI library cell without considering the state of communication port of each processing node which represents the processing node is in busy state or free state. If conflicting point-to-point communication occurs during broadcast communication, the transmitting speed for broadcast communication is decreased. Thus, this paper proposed an algorithm which determines the order of point-to-point communications for broadcast communication according to the state of each processing node. According to the state of each processing node, the proposed algorithm decreases total broadcast communication time by transmitting message preferentially to the processing node with communication port in free state. The proposed MPI unit for broadcast communication is evaluated by modeling it with systemC. In addition, it achieved a highly improved performance for broadcast communication up to 78% with 16 nodes. This result shows the proposed algorithm is useful to improving total performance of MPSoC.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37B no.9
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• pp.795-805
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• 2012

This paper proposes an algorithm and a hardware architecture for a broadcast communication which has the worst bottleneck among multiprocessor using distributed memory architectures. In conventional system, The pipelined broadcast algorithm is an algorithm which takes advantage of maximum bandwidth of communication bus. But unnecessary synchronization process are repeated, because the pipelined broadcast sends the data divided into many parts. In this paper, the MPI unit for pipeline chain algorithm based on circuit switching removing the redundancy of synchronization process was designed, the proposed architecture was evaluated by modeling it with systemC. Consequently, the performance of the proposed architecture was highly improved for broadcast communication up to 3.3 times that of systems using conventional pipelined broadcast algorithm, it can almost take advantage of the maximum bandwidth of transmission bus. Then, it was implemented with VerilogHDL, synthesized with TSMC 0.18um library and implemented into a chip. The area of synthesis results occupied 4,700 gates(2 input NAND gate) and utilization of total area is 2.4%. The proposed architecture achieves improvement in total performance of MPSoC occupying relatively small area.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.709-713
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• 2009

Recently, a lot of research that applies data acquired from devices such as cameras and RFIDs to context aware services is being performed in the field on Life-Log and the sensor network. A variety of analytical techniques has been proposed to recognize various information from the raw data because video and audio data include a larger volume of information than other sensor data. However, manually watching a huge amount of media data again has been necessary to create supervised data for the update of a class or the addition of a new class because these techniques generally use supervised learning. Therefore, the problem was that applications were able to use only recognition function based on fixed supervised data in most cases. Then, we proposed a method of acquiring supervised data from a video sharing site where users give comments on any video scene because those sites are remarkably popular and, therefore, many comments are generated. In the first step of this method, words with a high utility value are extracted by filtering the comment about the video. Second, the set of feature data in the time series is calculated by applying functions, which extract various feature data, to media data. Finally, our learning system calculates the correlation coefficient by using the above-mentioned two kinds of data, and the correlation coefficient is stored in the DB of the system. Various other applications contain a recognition function that is used to generate collective intelligence based on Web comments, by applying this correlation coefficient to new media data. In addition, flexible recognition that adjusts to a new object becomes possible by regularly acquiring and learning both media data and comments from a video sharing site while reducing work by manual operation. As a result, recognition of not only the name of the seen object but also indirect information, e.g. the impression or the action toward the object, was enabled.