Consistent with the trend towards the use of many cores in SOC and 3D Chip techniques, this paper proposes a "single-cycle ring" interconnection (SC_Ring) with ultra-low latency and minimal complexity. The proposed SC_Ring allows multiple single-cycle transactions in parallel. The main features of the circuit-switched design include a set of 3-ported circuit-switched routers (4~16) and a performance/timing effective arbiter. The arbiter, called "BTPC", features single-cycle arbitration and routing-control by means of the novel Binary-Tree paths convergence and path-prediction mechanisms, to provide a highly reduced time complexity. By combining this with the integration of 3D chips, the proposed ring-based interconnection offers several advantages for hierarchical clustering in future many-core systems, in terms of cost, latency, and power reductions. Moreover, based on the proposed SC_Ring, this work realizes a "level-1 non-uniform cache architecture" (L1-NUCA) for fast data communication without cache-coherency in facilitating multithreading/multi-core as a case study. Finally, experimental results show that our approach yields promising performance.

The selection and modification of kernel functions is a very important but rarely studied problem in the field of support vector learning. However, the kernel function of a support vector machine has great influence on its performance. The kernel function projects the dataset from the original data space into the feature space, and therefore the problems which can't be done in low dimensions could be done in a higher dimension through the transform of the kernel function. In this paper, we adopt the FCM clustering algorithm to group data patterns into clusters, and then use a statistical approach to calculate the standard deviation of each pattern with respect to the other patterns in the same cluster. Therefore we can make a proper estimation on the distribution of kernel functions. Experimental results have shown that our approach can derive better kernel functions than other methods, and also can have better learning and generalization abilities.