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.
Automated accelerator optimization aided by graph neural networks,
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Workshop report recommends NSF investments in AI-EDA collaboration, data infrastructure, compute resources, and workforce development to accelerate hardware design.
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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.
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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.