HLS-Seek replaces full-synthesis RL with a comparative proxy reward model plus uncertainty-triggered real checks, yielding higher correctness and better QoR than larger models at 8.5x lower training cost.
arXiv preprint arXiv:2507.03255abs/2507.03255 (2025), 1–15
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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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HLS-Seek: QoR-Aware Code Generation for High-Level Synthesis via Proxy Comparative Reward Reinforcement Learning
HLS-Seek replaces full-synthesis RL with a comparative proxy reward model plus uncertainty-triggered real checks, yielding higher correctness and better QoR than larger models at 8.5x lower training cost.
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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.