Abstract
Parallel computing and many-core Network-on-Chip (NoC) technology are increasingly popular to accelerate performance. One way to accomplish this is by simultaneously deploying multiple applications on the NoC. Applications are deployed in NoCs using application mapping, which is considered a critical design process due to its NP-hard nature. The application mapping problem has been addressed in many ways. However, most of the efforts have focused on single application mapping, and only a few multi-application mapping approaches have been developed. This work proposes a multi-application mapping approach based on Artificial Neural Networks (ANN) for mesh NoC. In order to evaluate the mapping strategy, communication costs and CPU run-time are taken into account. Our approach is able to reduce communication costs by 80% or more than the communication cost reported in the literature.The proposed approach results demonstrated that the proposed approach could solve application mapping problems without high complexity or computational cost. In future works, the proposed approach will be integrated with a reconfigurable approach for solving multi-application problems with better performance parameters.
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Authors thank the Center for Development of Advanced Computing, India for their support and encouragement in doing this work.
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Choudhary, J., Bindal, V., Soumya, J. (2022). MANA:Multi-Application Mapping onto Mesh Network-on-Chip using ANN. In: Shah, A.P., Dasgupta, S., Darji, A., Tudu, J. (eds) VLSI Design and Test. VDAT 2022. Communications in Computer and Information Science, vol 1687. Springer, Cham. https://doi.org/10.1007/978-3-031-21514-8_24
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