EMSOA based application mapping framework for power optimisation in 3D-NoC

Application mapping is a difficult and crucial topic of research in Networks on Chip (NoC). Mapping of NoC is an NP-hard issue and various types of heuristics have been adopted to deal these issues. Therefore, in this research, Hesitant Fuzzy Linguistic Bi-objective Clustering Method (HFLBCM) with Evolutionary Multi-objective Seagull Optimisation Algorithm (EMSOA) is proposed for application mapping in 3D-NoC. The robustness of proposed method is confirmed by experiments conducted on certain benchmarks, like VOPD and MPEG-4 in NoC. At first, the HFLBCM approach selects the core and calculates the weight of each core. Subsequently, EMSOA method performs the application mapping with standard topologies, like mesh and torus. The implementation of the proposed method is done in Python and the parameters, like power, delay and computation time for VOPD and MPEG-4 benchmarks. The proposed approach has attained 45.175%, 65.319%, 71.226% lower power consumption for mesh topology and 30.141%, 19.211%, 32.727% lower power consumption for torus topology in VOPD benchmark than existing methods, like Knowledge-base multiple objective application mapping approach (AM-3D NoC-KBMA), self-adaptive mapping approach for NoC with low power consumption (AM-3D NoC-SKNNCM-SACSOA) and Ant lion optimised buffer less routing design for low power application specific NoC (AM-3D NoC-ALOA), respectively.