This dissertation proposes different methodologies, with their associated models, to customize the architectural design of Application-Specific Networks-on-Chip (ASNoC). Specifically, system-level evaluation models are presented and architecture generation methodologies are built on them to allow the designer to generate the most efficient architecture for a given NoC-based application. Our system-level methodologies enable the designer to discover any flaws early during the design process and to quickly investigate the effect of various design choices on the resultant NoC cost and performance. In this dissertation, we have four main contributions.;In our first contribution, we propose power and reliability evaluation models. The two models are proposed at the system-level to allow for a quick evaluation of different design decisions. The power model captures the power consumption in NoC routers and links, whereas the reliability one models the probability of the packets being affected by on-chip noise sources.;In our second contribution, we propose a cost-efficient architecture generation methodology for NoC based on network partitioning techniques. Our methodology partially customizes the on-chip network architecture with respect to two cost metrics: power and area. The partitioning technique is formulated using NoC terminology based on the Fiduccia-Mattheyses graph partitioning algorithm. Our partitioning scheme is compared to other partitioning techniques and is found to be the most efficient one for NoC. We further analyze the effect of using network partitioning on NoC power, area, and delay. From this analysis, the area reduction is proved to be guaranteed using network partitioning. Moreover, power and delay efficiencies of using network partitioning with NoC are formulated mathematically. Experimental results show that the proposed methodology is an efficient way to reduce power and area costs of NoC with respect to both standard and previous custom architecture generation techniques.;In our third contribution, we propose a multi-objective Genetic Algorithm (GA)-based optimization methodology for NoC full-custom architectures. For any application, the designer could control the optimization process through different optimization weight factors. Our methodology is evaluated by applying it to different NoC benchmark applications, as case studies. Results show that the architectures generated by our methodology outperform those generated by other techniques with respect to power, area, delay, reliability, and the combination of the four metrics. Finally, the running time of our methodology is an order of magnitude faster than that of previous architecture optimization techniques.;In our fourth contribution, we propose a multi-objective GA-based methodology to optimize the use of standard architectures, which were previously presented in computer network, with NoC. Our methodology combines the best selection of NoC standard architecture and the optimum mapping of application cores onto that architecture. The methodology is further used to carry out an application-specific mapping-oriented evaluation of different NoC standard architectures. Experimental results show that the mapping achieved by our methodology outperforms those generated by previous mapping techniques with respect to power, area, delay, reliability, and the combination of the four metrics.;This research work aims at quickly validating various design decisions by proposing system-level power and reliability evaluation models. Moreover, in this dissertation, we present three application-specific methodologies to customize the three main categories of architectures that are currently used in implementing on-chip networks; namely, semi-custom, full-custom, and standard architectures, respectively. Our methodologies consider different NoC metrics: power, area, delay, and reliability, simultaneously. We believe that our proposed methodologies bridge an open gap in NoC research by matching the on-chip network architecture to the characteristics and the rapidly growing requirements of modern NoC applications.
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