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Hybrid multi-swarm optimization based NoC synthesis

机译:基于混合多群优化的NoC合成

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Network-on-Chip (NoC) has been proposed as an interconnection framework for connecting large number of cores for a System-on-Chip (SoC). Assuming a mesh-based NoC, we explore the assignment of cores to cross-points and produce a best NoC configuration with minimum average communication traffic, power consumption and chip area. We use pre-synthesized network components data to estimate power and chip area of the NoC. NoC configuration and mapping problem belongs to NP-hard complexity set, therefore we propose a hybrid scheme of swarm optimization that combines Tabu-search, force-directed swapping, sub-swarms, and Discrete Particle Swarm Optimization (DPSO). The main goal of the optimization is to configure the NoC such that the total NoC latency, power consumption, and area occupied are minimal. DPSO is used as the main optimization scheme and modified so that each particle move is also influenced by a force derived from the NoC traffic matrix. The methodology is tested for some multimedia application core graphs as well as large network of randomly generated cores. It is determined that on average our hybrid technique required less number of iterations and time to reach an optimal solution when compared with existing NoC synthesis algorithms.
机译:片上网络(NoC)已被提议作为一种互连框架,用于连接片上系统(SoC)的大量内核。假设基于网状网络的NoC,我们探索将核心分配给交叉点,并以最小的平均通信流量,功耗和芯片面积产生最佳的NoC配置。我们使用预先合成的网络组件数据来估计NoC的功率和芯片面积。 NoC配置和映射问题属于NP-hard复杂度集,因此,我们提出了一种组合了禁忌搜索,强制定向交换,子群和离散粒子群优化(DPSO)的群优化混合方案。优化的主要目标是配置NoC,以使总NoC延迟,功耗和占用面积最小。 DPSO被用作主要的优化方案并进行了修改,以使每个粒子的移动也受到来自NoC流量矩阵的力的影响。该方法已针对某些多媒体应用程序核心图以及随机生成的核心的大型网络进行了测试。可以确定,与现有的NoC合成算法相比,我们的混合技术平均需要较少的迭代次数和时间才能达到最佳解决方案。

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