AI4DSE: Leveraging Dynamic Graph Neural Networks and Large Language Models for Optimizing High-Level Synthesis Design Space Exploration

High-Level Synthesis (HLS) Design Space Exploration (DSE) is essential for generating hardware designs that balance performance, power, and area (PPA). To optimize this process, existing works often employs message-passing neural networks (MPNNs) to predict quality of results (QoR). These predictors serve as evaluators in the DSE process, effectively bypassing the time-consuming estimations traditionally required by HLS tools. However, existing models based on MPNNs struggle with over-smoothing and limited expressiveness. Additionally, while meta-heuristic algorithms are widely used in DSE, they typically require extensive domain-specific knowledge to design operators and time-consuming tuning. To address these limitations, we propose ECoGNNs-LLMMHs, a framework that integrates graph neural networks with task-adaptive message passing and large language model-enhanced meta-heuristic algorithms. Compared with state-of-the-art works, ECoGNN exhibits lower prediction error in the post-HLS prediction task, with the error reduced by 57.27%. For post-implementation prediction tasks, ECoGNN demonstrates the lowest prediction errors, with average reductions of 17.6% for flip-flop (FF) usage, 33.7% for critical path (CP) delay, 26.3% for power consumption, 38.3% for digital signal processor (DSP) utilization, and 40.8% for BRAM usage. LLMMH variants can generate superior Pareto fronts compared to meta-heuristic algorithms in terms of average distance from the reference set (ADRS) with average improvements of 87.77%, respectively. Compared with the SOTA DSE approaches GNN-DSE and IRONMAN-PRO, LLMMH can reduce the ADRS by 68.17% and 61.53% respectively. Code and models are available at https://github.com/wslcccc/CoGNNs_LLMMH.