Optimization Strategy of Regular NoC Mapping Using Genetic-Based Hyper-Heuristic Algorithm

Abstract

Mapping optimization of network-on-chips (NoCs) for specific applications has become one of the most important keys of the SoC top-level design. However, the topology of NoC applied is usually regular topology, such as mesh, torus, etc., which may generate a large number of isomorphic solutions during the process of NoC mapping, which may reduce the convergence speed of mapping algorithms. In this paper, we proposed a generic-based hyper-heuristic algorithm named IRC-GHH for NoC mapping. To reduce the influence of isomorphic solutions, we analyzed the symmetry of NoC topology and proposed crossover operators based on the isomorphic solution to optimize the algorithm. We studied the situation of invalid crossovers and eliminated invalid iterations by adopting an isomorphic replacement crossover (IRC) strategy. To prevent the algorithm from falling into evolutionary stagnation in the late iteration, we introduce an adaptive mechanism to increase the usage frequency of the IRC operator automatically. Compared with GHH without IRC, the GHH with IRC can achieve, on average 15.25% communication energy reduction and 7.84% communication delay reduction.