Mohammad Masdari

One of the main stages of Network on Chip (NoC) is task mapping where the application tasks are mapped to the processing resources. Task mapping decisions have a direct impact on the NoC performance and can be classified as static or dynamic mapping methods. In this paper, we carry out a comprehensive systematic study of task mapping schemes in various NoCs. For this purpose, firstly, an overview of the task mapping's main concepts is presented. Then, a taxonomy of the mapping schemes regarding their objectives and applied mapping factors is presented and the studied schemes are categorized into fault-tolerant schemes, congestion-aware schemes, communication-aware approaches, temperature-aware methods, and energy-aware schemes. Afterward, their main contributions, provided services, and drawbacks of the proposed studies in the literature are illustrated. Moreover, a comprehensive discussion of various features of the investigated mapping methods is presented and issues such as the applied algorithms, simulators, datasets, and evaluation metrics are discussed. Finally, the concluding issues and open research areas are highlighted.

The African Vulture Optimization Algorithm (AVOA) is inspired by African vultures’ feeding and orienting behaviors. It comprises powerful operators while maintaining the balance of exploration and efficiency in solving optimization problems. To be used in discrete applications, this algorithm needs to be discretized. This paper introduces two versions based on the S-shaped and V-shaped transfer functions of AVOA and BAOVAH. Moreover, the increase in computational complexity is avoided. Disruption operator and Bitwise strategy have also been used to maximize this model’s performance. A multi-strategy version of the AVOA called BAVOA-v1 is presented. In the proposed approach, i.e., BAVOA-v1, different strategies such as IPRS, mutation neighborhood search strategy (MNSS) (balance between exploration and exploitation), multi-parent crossover (increasing exploitation), and Bitwise (increasing diversity and exploration) are used to provide solutions with greater variety and to assure the quality of solutions. The proposed methods are evaluated on 30 UCI datasets with different dimensions. The simulation results showed that the proposed BAOVAH algorithm performed better than other binary meta-heuristic algorithms. So that the proposed BAOVAH algorithm set is the most accurate in 67% of the data set, and 93% of the data set is the best value of the fitness functions. In terms of feature selection, it has shown high performance. Finally, the proposed method in a case study to determine the number of neurons and the activator function to improve deep learning results was used in the sentiment analysis of movie viewers. In this paper, the CNNEM model is designed. The results of experiments on three datasets of sentiment analysis—IMDB, Amazon, and Yelp—show that the BAOVAH algorithm increases the accuracy of the CNNEM network in the IMDB dataset by 6%, the Amazon dataset by 33%, and the Yelp dataset by 30%.