Late Breaking Results: Language-level QoR modeling for High-Level Synthesis
Dimosthenis Masouros, Aggelos Ferikoglou, Georgios Zervakis, Sotirios Xydis, Dimitrios Soudris · 2024
This paper proposes a language-level modeling approach for HighLevel Synthesis based on the state-of-the-art Transformer architecture. Our approach estimates the performance and required resources of HLS applications directly from the source code when different synthesis directives, in terms of HLS #pragmas, are applied. Results show that the proposed architecture achieves 96.02% accuracy for predicting the feasibility class of applications and an average of 0.95 and 0.91 R2 scores for predicting the actual performance and required resources, respectively.