Wavefront-MCTS: Multi-objective Design Space Exploration of NoC Architectures based on Monte Carlo Tree Search

Abstract

Application-specific MPSoCs profit immensely from a custom-fit Network-on-Chip (NoC) architecture in terms of network performance and power consumption. In this paper we suggest a new approach to explore application-specific NoC architectures. In contrast to other heuristics, our approach uses a set of network modifications defined with graph rewriting rules to model the design space exploration as a Markov Decision Process (MDP). The MDP can be efficiently explored using the Monte Carlo Tree Search (MCTS) heuristics. We formulate a weighted sum reward function to compute a single solution with a good trade-off between power and latency or a set of max reward functions to compute the complete Pareto front between the two objectives. The Wavefront feature adds additional efficiency when computing the Pareto front by exchanging solutions between parallel MCTS optimization processes. Comparison with other popular search heuristics demonstrates a higher efficiency of MCTS-based heuristics for several test cases. Additionally, the Wavefront-MCTS heuristics allows complete tracability and control by the designer to enable an interactive design space exploration process.