Published June 11, 2024
| Version v1
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Language-level QoR Modeling for High-Level Synthesis
Creators
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1.
National Technical University of Athens
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2.
University of Patras
Description
This paper proposes a language-level modeling approach for High-Level 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 R^2 scores for predicting the actual performance and required resources, respectively.
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Language_level_QoR_modeling_for_High_Level_Synthesis.pdf
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