The IP core mapping optimization problem is an NP-hard problem in network-on-chip design. Because of the computational complexity of an IP core mapping, the MPNN-Ptr networks composed of the graph networks and the pointer networks are proposed to model the IP core mapping. The neural network IP core mapping model (NNMM) can effectively evaluate the probability of each mapping solution as the optimal solution to an IP core mapping optimization problem. Then, an IP core mapping algorithm, the neural mapping algorithm (NMA), is proposed. In this algorithm, the global search is realized by sampling the mapping solutions with a high probability evaluated by NNMM. The sampled candidate mapping solutions according to probability can effectively decrease the candidate mapping solution space, which can reduce invalid searches and avoid getting stuck at local minima. Then, the 2-opt algorithm is used as a local search to improve the quality of mapping further. Simulation results show that the MPNN-Ptr networks can effectively model the IP core mapping. Compared with the state-of-the-art mapping algorithms, NMA produces better solutions for both specific applications and random applications. NMA achieves a 7.90% communication cost reduction on average than the classic mapping algorithm: NMAP.

中文翻译：

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基于神经网络的IP核映射算法

IP核心映射优化问题是片上网络设计中的NP难题。由于IP核心映射的计算复杂性，提出了由图形网络和指针网络组成的MPNN-Ptr网络对IP核心映射进行建模。神经网络IP核心映射模型（NNMM）可以有效地评估每个映射解决方案作为IP核心映射优化问题的最佳解决方案的可能性。然后，提出了一种IP核心映射算法，即神经映射算法（NMA）。在该算法中，通过以NNMM评估的高概率对映射解进行采样来实现全局搜索。根据概率采样的候选映射解可以有效地减少候选映射解空间，这样可以减少无效搜索并避免陷入局部最小值。然后，将2-opt算法用作局部搜索以进一步提高映射质量。仿真结果表明，MPNN-Ptr网络可以有效地对IP核心映射进行建模。与最新的映射算法相比，NMA为特定应用程序和随机应用程序提供了更好的解决方案。与经典的映射算法NMAP相比，NMA平均可降低7.90％的通信成本。