A Reinforcement Learning Based Multi-objective Evolutionary Algorithm for High-level Synthesis Design Space Exploration

By applying various optimization directives (e.g., loop unrolling, function inlining), high-level synthesis (HLS) enables designers to generate diverse hardware implementations with varying performance and resource trade-offs. The interplay of these directives significantly influences the performance and cost of the synthesized designs, making Design Space Exploration (DSE) crucial for identifying configurations that yield Pareto-optimal solutions. To address this complex multi-objective optimization challenge, we propose an approach that integrates the evolutionary algorithm with reinforcement learning. Specifically, Q-learning is employed to adaptively optimize the exploration strategy throughout the search process, while the evolutionary algorithm effectively balances global and local searches. Compared to state-of-the-art DSE approaches, experimental results demonstrate that our proposed approach achieves better performance in synthesis runs, synthesis time, and final ADRS, with improvements of 21.00%-34.00%, 25.22%-38.22%, and 80.80%97.00%, respectively, and achieves 115X-837X speedup.