3DMIA: A Multi-objective Artificial Immune Algorithm for 3D-MPSoC Multi-Application 3D-NoC Mapping

Three dimensional Multiprocessor System-on-Chip (3D-MPSoC) are characterized by the integration of a large amount of hardware components targeting a wide range of application on a single chip. However, heating is one of the major pitfalls of the 3D-MPSoCs. Three dimensional Network-on-Chip (3D-NoC) is used as the communication structure of the 3D-MPSoC. Its main role in the system operation and performance turns critical the optimal 3D-NoC design. Mapping is one of the most critical 3D-NoC parameters, strongly influencing the 3D-MPSoC performance. In this paper we propose the use of a multi-objective immune algorithm (3DMIA), an evolutionary approach to solve the multi-application 3D-NoC mapping problem. Latency and power consumption were adopted as the target multi-objective functions constrained by the heating function. Final 3D-NoC configurations enhance up to 73 % the power and 42 % the latency when compared to the previous reported results. We also evaluate the effect on the mutation rate and population size on the convergence speed of 3DMIA. We find that the adaptive mutation rate increases the performance of 3DMIA up to 84 % when compared to static mutation rate approach