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Paolo Meloni, Igor Loi, Federico Angiolini, Salvatore Carta, Massimo Barbaro, Luigi Raffo, Luca Benini, "Area and Power Modeling for Networks-on-Chip with Layout Awareness", VLSI Design, vol. 2007, Article ID 050285, 12 pages, 2007. https://doi.org/10.1155/2007/50285
Area and Power Modeling for Networks-on-Chip with Layout Awareness
Networks-on-Chip (NoCs) are emerging as scalable interconnection architectures, designed to support the increasing amount of cores that are integrated onto a silicon die. Compared to traditional interconnects, however, NoCs still lack well established CAD deployment tools to tackle the large amount of available degrees of freedom, starting from the choice of a network topology. “Silicon-aware” optimization tools are now emerging in literature; they select an NoC topology taking into account the tradeoff between performance and hardware cost, that is, area and power consumption. A key requirement for the effectiveness of these tools, however, is the availability of accurate analytical models for power and area. Such models are unfortunately not as available and well understood as those for traditional communication fabrics. Further, simplistic models may turn out to be totally inaccurate when applied to wire dominated architectures; this observation demands at least for a model validation step against placed and routed devices. In this work, given an NoC reference architecture, we present a flow to devise analytical models of area occupation and power consumption of NoC switches, and propose strategies for coefficient characterization which have different tradeoffs in terms of accuracy and of modeling activity effort. The models are parameterized on several architectural, synthesis-related, and traffic variables, resulting in maximum flexibility. We finally assess the accuracy of the models, checking whether they can also be applied to placed and routed NoC blocks.
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Copyright © 2007 Paolo Meloni et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.