## 2 Related Work  
In the current section, we briefly look into the current approaches to application mapping challenges for NoC design from several perspectives, namely, minimizing communication energy, enhancing performance, and reducing computation time. Small-scale IP mapping issues can be formulated as integer programming problems [15; 16] and resolved using the branch and bound approach [17; 18]. However, neither approach can effectively address extensive IP mapping issues due to their lengthy computation times. The most prominent approach for large-scale IP mapping issues is heuristic search, which may be loosely split into two kinds: transformative heuristics and constructive heuristics [13]. Typically, heuristic approaches map cores onto routers using pre-established criteria. NMAP, an efficient NoC application mapping approach [19], maps the application tasks to the cores in three phases: initialization, minimum path computation, and pair-wise swapping until the optimum mapping solution is obtained. BMAP is a greedy binomial mapping method [20] in which a candidate solution is first proposed, followed by iterative improvement until the final mapping is achieved. To achieve more solutions, CastNet generates various alternative solutions for each core depending on the availability of adjacent free neighbors by using multiple tiles to work as initial tiles, leveraging the symmetric features of the mesh [21]. In CHMAP (constructive heuristic mapping approach) [22], the root is chosen as the core with the highest average communication traffic, then a maximum spanning tree is constructed from the communicative graph. CHMAP calculates the mapping order based on the degree and distance of the cores and then assigns each core to the most suitable router based on mapping criteria. CastNet and CHMAP do not employ further iterative-based improvements upon the initial solution to reach the best possible solution. Transformative algorithms often use evolutionary approaches to look for approximations. Examples of common evolutionary algorithms [6] for optimization encompass genetic algorithms (GAs), discrete particle swarm optimization (DPSO), and ant colony algorithms. A DPSO-based technique that employs a multi-stage PSO and certain deterministic particles for the initial population rather than random ones was reported in [14]. Optimized Mapping solutions for both 2D and 3D NoC were presented. A novel optimization technique grounded on the DPSO framework was introduced in [23], where the velocity update process included a perturbation particle and an elite particle introduced to facilitate the exploration of local optima. The algorithm allows particles to switch between elite and conventional pools, and uses a simplified local search of elite particles to find potential solutions. Multi-application mapping based on a reconfigurable NoC architecture was reported in [24], where a Mesh of Tree (MoT) topology was implemented. A two-step efficient Particle Swarm Optimization (PSO) approach was used to reduce the communication cost for a reconfigurable architecture. A contention-aware genetic algorithm-based application mapping solution was reported in [25], where both the spatial and temporal attributes of communication were considered in order to optimize performance by avoiding contention. To optimize the NoC in terms of communication cost, average latency, and energy, an efficient mapping technique was proposed [26] using the cuckoo search technique with Levy flight. First, a greedy algorithm was used to place the most communicative tasks together for an initial quality solution. Levy flying meta-heuristic cuckoo search was then used to optimize task placements for optimum mapping. In [27], the authors put forward the IHPSA optimization method, an enhanced hybrid PSO, and the simulated annealing technique. Enhanced Particle Swarm Optimization with SA was combined in the proposed IHPSA for application mapping. The K-means clustering machine learning method arranges tasks based on their communication bandwidth.