DiffHLS predicts HLS QoR via differential learning: separate GNN+LLM models for kernel baseline and design delta are composed to yield the final estimate, showing lower MAPE than GNN baselines on PolyBench.
Robust GNN-based representation learning for HLS
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Workshop report recommends NSF investments in AI-EDA collaboration, data infrastructure, compute resources, and workforce development to accelerate hardware design.
citing papers explorer
-
DiffHLS: Differential Learning for High-Level Synthesis QoR Prediction with GNNs and LLM Code Embeddings
DiffHLS predicts HLS QoR via differential learning: separate GNN+LLM models for kernel baseline and design delta are composed to yield the final estimate, showing lower MAPE than GNN baselines on PolyBench.
-
Report for NSF Workshop on AI for Electronic Design Automation
Workshop report recommends NSF investments in AI-EDA collaboration, data infrastructure, compute resources, and workforce development to accelerate hardware design.