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.
In: Proceedings of the 2024 ACM/IEEE International Symposium on Machine Learning for CAD, pp
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A literature survey finds foundation-model agents in industry are 75% at prototype stages with gains in human interaction and uncertainty handling but deficits in negotiation, plus limitations like hallucinations and latency.
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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.