arXiv:2611.07324v1 Announce Type: new Abstract: Large language models (LLMs) offer a promising path for directly generating functionally correct and synthesizable HLS-C from natural-language specifications. However, existing approaches often struggle to turn multi-round tool feedback and reusable HLS knowledge into effective revisions, limiting end-to-end generation success. We propose MementoHLS, an agentic HLS generation framework built around executable and falsifiable dual-memory-guided repair.
Computer Science · 2026-11-06
Rule-Guided and Trajectory-Aware Repair for LLM-Based HLS Generation
arXiv:2611.07324v1 Announce Type: new Abstract: Large language models (LLMs) offer a promising path for directly generating functionally correct and synthesizable HLS-C from natural-language specifications. However, existing approaches often struggle to turn multi-round tool feedback and reusable HLS knowledge into effective revisions, limiting end-to-end generation success.
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Original source: arXiv Computer Science AR ↗
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