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۱A New Efficient Routing Algorithm for Network–on–Chip
اطلاعات انتشار: شانزدهیمن کنفرانس مهندسی برق ایران، سال
تعداد صفحات: ۶
The performance of Network–on–Chip (NoC) largely depends on the underlying routing techniques, which have two constituencies: output selection and input selection. In this paper a novel routing algorithm for network on chip, which called BIOS, is presented. Previous routing algorithms for network on chip focused only on improvement of output selection. But BIOS improves both input and output selection. In fact, BIOS is based on the best input and output selection. Simulation results with different traffic patterns show that our new routing algorithm(BIOS) achieves significant better performance than the other deterministic and adaptive routing algorithms because BIOS combines the advantages of both deterministic and adaptive routing algorithms.<\div>

۲Analysis of Support Vector Machines and Kernel Functions
نویسنده(ها): ،
اطلاعات انتشار: سیزدهمین کنفرانس دانشجویی مهندسی برق ایران، سال
تعداد صفحات: ۸
Support Vector Machine (SVM) is one of the classification methods in machine learning. It shows excellent performance in many pattern recognition applications. SVM map an input sample into a high dimensional feature space and try to find an optimal hyperplane. Although it has some challenges that one of them is non linear models, but a model can be mapped to a new space by doing a nonlinear transformation then use a linear model in this new space can be solved the problem. In this paper we explain some important aspects to reach the best performance such as: kernel functions and selecting them, data normalization, multiclass support vector machines, and applications. Since delay and accuracy are the important parameters to improve the performance in SVMs, we compare some of the combined algorithms with these parameters to use the best algorithms in our future works. Finally some directions for researches are provided.<\div>

۳A New Fuzzy Input Selection Technique to Increase Routing Efficiency for Network–On–Chip
اطلاعات انتشار: نوزدهمین کنفرانس مهندسی برق ایران، سال
تعداد صفحات: ۶
The performance of Network–On–Chip(NOC) largely depends on the underlying routing techniques. A routing technique has two constituencies: output selection and input selection. This paper focuses on the improvement of input selection part. Two traditional input selections have been used in NOC, First–Come–First–Served (FCFS) input selection and Round–Robin input selection. Also, recently a contention–aware input selection (CAIS) technique has been presented for NOC, But there is some problem and defects in this technique. In this paper we eliminate the problems and defects of CAIS technique to develop a simple yet effective input selection technique named FCAIS. When there are contentions of multiple input channels competing for the same output channel, FCAIS decides which input channel obtains the access depending on the two parameters :contention level of the upstream switches and AGE of the all input channels. In this scheme each switch selects one of the input channels with highest priority, which is calculated by a fuzzy controller. The simulation results with different traffic patterns show that FCAIS can achieves better performance than the FCFS and CAIS input selections, when combined with either deterministic or adaptive output selection.<\div>

۴Resource Reservation in Grid Vetworks based on Irregular Cellular Learning Automata
نویسنده(ها): ،
اطلاعات انتشار: International Journal Information and Communication Technology Research، هفتم،شماره۳، Summer ۲۰۱۵، سال
تعداد صفحات: ۹
Computing infrastructures that are based on grid networks have been recognized as a basis for new infrastructures of distributed computing. Providing appropriate mechanisms for scheduling and allocating resources to user’s requests in these networks is considered very important. One of the current issues in the grid networks is how to ensure the precise timing of executing requests sent by users, especially requests that have deadlines and also co–allocation requests. The resource reservation has been mainly developed to address this problem in the grid systems. On the other hand, models based on the cellular automata have advantages such as lower processing complexity, configurability of the cells, and the ability of predicting future conditions. In this study, an efficient model based on irregular cellular learning automata (ICLA) is presented for the task of resource reservation. The proposed model was simulated on a network with random topology structure. The performance of proposed method was compared with two well–known algorithms in this field. The experimental results showed increased efficiency in the resource utilization, decreased process execution delays, and reduced rate of request rejection.
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