Presentation
Late Breaking Results: Language-level QoR modeling for High-Level Synthesis
DescriptionThis paper proposes a language-level modeling approach for HLS based on the state-of-the-art Transformer architecture. Our approach estimates the performance and resource requirements of HLS applications directly from the source code when different synthesis directives, in terms of HLS directives, 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 R^2 scores for predicting the actual performance and required resources, respectively.
Event Type
Late Breaking Results Poster
TimeWednesday, June 266:00pm - 7:00pm PDT
LocationLevel 2 Lobby