TMM-DSE: Topology-Aware MLP-Mixer for QoR Prediction in HLS Design Space Exploration

Multi-layer perceptron (MLP) is often the preferred model for regression tasks. Recently, graph-based networks (GNN) have become popular for estimating post-implementation Quality of Results (QoR) in High-Level Synthesis (HLS). In addition to the node features composed of various HLS directives, local neighborhood information and global information in the topology are crucial for accurate QoR prediction. In this paper, we propose a Topology-aware MLP-Mixer Predictor (TMMP) based exclusively on an MLP structure to estimate postimplementation QoR. TMMP leverages correlation encoding and a feature-mixing structure to achieve the capability of extracting topological information similar to that of GNN. Combining this with a Gaussian Process-based Multi-objective Bayesian Optimization engine (GMBO), we ultimately achieve a design space exploration framework, TMM-DSE, which can rapidly and accurately identify optimal design parameters. Experimental results show that our simple and efficient TMMP achieves area prediction performance comparable to the current best GNNbased models, with power and critical path delay errors only between 4.64 % and 6.77 %, even surpassing the SOTA work. GMBO with TMMP can accelerate the DSE process by up to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$69 \times$</tex>, with an average speedup of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$35 \times$</tex>.