RAMAN: Reinforcement Learning Inspired Algorithm for Mapping Applications onto Mesh Network-on-Chip

Abstract

Application Mapping in Network-on-Chip (NoC) design is considered a vital challenge because of its NP-hard nature. Many efforts are made to address the application mapping problem, but none has satisfied all the requirements. For example, Integer Linear Programming (ILP) has achieved the best possible solution but lacks scalability. Advancements in Machine Learning (ML) have added new dimensions in solving the application mapping problem. This paper proposes RAMAN: Reinforcement Learning (RL) inspired algorithm for mapping applications onto mesh NoC. RAMAN is a modified Q-Learning technique inspired by RL, aiming to achieve the minimum communication cost for the application mapping problem. The results of RAMAN demonstrated that RL has enormous potential to solve application mapping problem without much complexity and computational cost. RAMAN has achieved the communication cost within the 6% of the optimal cost determined by ILP. Considering the computational overheads and complexity, the results of RAMAN are encouraging. Future work will improve RAMAN's performance and provide a new aspect to solve the application mapping problem.