论文阅读笔记 Xie_Deep Learning Enabled Semantic Communication System 共计9页，详细手写

Abstract On-chip interconnects are predicted to be a fundamental issue in designing multi-core chip multiprocessors (CMPs) and system-on-chip (SoC) architectures with numerous homogeneous and heterogeneous cores and functional blocks. To mitigate the interconnect crisis, one promising option is network-on-chip (NoC), where a general purpose on-chip interconnection network replaces the traditional design-specific global on-chip wiring by using switching fabrics or routers to connect IP cores or processing elements. Such packet-based communication networks have been gaining wide acceptance due to their scalability and have been proposed for future CMPs and SoC design. In this chapter, we study the combination of both three-dimensional integrated circuits and NoCs, since both are proposed as solutions to mitigate the interconnect scaling challenges. This chapter will start with a brief introduction on network-on-chip architecture and then discuss design space exploration for various network topologies in 3D NoC design, as well as different techniques on 3D on-chip router design. Finally, it describes a design example of using 3D NoC with memory stacked on multi-core CMPs.

针对核空间中模糊聚类算法的有效性评价问题,以核非线性映射为工具,将Xie-Beni指标推广到核Hilbert空间,得到其对应的核化形式,并指出该核化指标与VLL指标的区别和联系.在此基础上,通过比较实验,研究了核化的Xie-Beni指标对高斯核宽度β和模糊指数m的稳定特性.结果表明,核化的Xie-Beni指标较之VLL等其他指标具有更好的性能和可靠性,可优先作为核模糊聚类算法的有效性判据.

机器学习评价内蕴方法，Xie-Beni指标是模糊K均值算法的聚类有效指标

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