MailoHLS combines LLM semantic reasoning and GNN structural modeling with multi-adapter PEFT and Pareto optimization to produce near-Pareto-optimal HLS pragma configurations, reporting up to 12.42x latency speedup on seen kernels and 10.2x on unseen ones.
Hlspilot: Llm-based high-level synthesis
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
ChipSeek is a hierarchical-reward reinforcement learning framework with Curriculum-Guided Dynamic Policy Optimization that integrates EDA simulator feedback to improve LLM-generated RTL code on both functional correctness and PPA metrics.
citing papers explorer
-
MailoHLS: Multi-Adapter Structure-Aware Learning for Pareto-Driven HLS Pragma Optimization
MailoHLS combines LLM semantic reasoning and GNN structural modeling with multi-adapter PEFT and Pareto optimization to produce near-Pareto-optimal HLS pragma configurations, reporting up to 12.42x latency speedup on seen kernels and 10.2x on unseen ones.
-
ChipSeek: Optimizing Verilog Generation via EDA-Integrated Reinforcement Learning
ChipSeek is a hierarchical-reward reinforcement learning framework with Curriculum-Guided Dynamic Policy Optimization that integrates EDA simulator feedback to improve LLM-generated RTL code on both functional correctness and PPA metrics.