An IP Core Mapping Algorithm Based on Neural Networks

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.