Heuristic algorithm for task mapping problem in a hierarchical wireless network-on-chip architecture

Given the complexity and wide range of applications being developed for the Internet of Things (IoT) requiring an efficient mapping of application tasks in hardware resources, especially embedded systems, it is necessary to address the problem of resource optimization, taking into account constraints for energy consumption, latency, and bandwidth, among others. This paper proposes a population based multi-objective heuristic algorithm to solve the task mapping for an application into a hierarchical hardware architecture known as Wireless Network on Chip using a mathematical modeling, as well as Artificial Intelligence algorithms, that combine the strategies of neural networks and genetic algorithms. A Population Based Incremental Learning (PBIL) algorithm is used in this research to solve the task mapping problem oriented to IoT applications, allowing heterogeneous systems to be involved with different bandwidths, latencies and technologies. The optimization goals addressed in this paper are bandwidth, speedup, power consumption and communication cost. The PBIL algorithm was tested on synthetic applications with heavy workload, where performance is measured taking into account the combination of low power consumption and maximum speedup and bandwidth. Other tests carried out with the PBIL algorithm were aimed to reduce the cost of communication in task mapping for real applications such as Multi-Window Display (MWD) and MPEG4 decoder. The results obtained in both synthetic and real applications showed an improvement when Escort entropy was used compared to Renyi and Shannon. In the case of real applications, when using a 2D Mesh topology, an improvement of 15.25% for MWD and 40.32% for MPEG-4 was observed when compared to the ISFL algorithm. Also, when the Mesh-Star topology was used, better results were obtained compared to ISFL (48.74% MWD and 62.06% MPEG-4).