Multi-objective artificial immune algorithm for security-constrained multi-application NoC mapping

Current SoC (System-on-Chip) are characterized by the integration of larger amount of IPs targeting a wide range of application fields. Such multi-application systems are constrained by a set of security and performance requirements. Network-on-chip (NoC) is becoming important as the communication structure of the SoC. IP mapping is one of the most critical parameters in NoC design, strongly influencing the SoC performance. IP mapping has been solved for single application systems using single and multi-objective optimization algorithms. In this paper we propose the use of a multi-objective adaptive immune algorithm (M2AIA), an evolutionary approach to solve the multi-application NoC mapping problem targeting security issues, in order to group the IPs according the security characteristics while achieving the best performance. Latency and power consumption were adopted as the target multi-objective functions constrained by the security function. To compare the efficiency of our approach, our results are compared with those of the genetic and branch-and-bound multi-objective mapping algorithms. The experimental results showed that the M2AIA achieves configurations that fulfill the security requirements while decreasing the power consumption in 27% and the latency in 42% compared to the branch-and-bound approach and 29% and 36% over the genetic approach.