MechMem-RTL: Reusing Verified Mechanism Memories for LLM-Based RTL Repair

2026-07-18

AI Summary

Existing large language model (LLM)-based RTL repair approaches rely on textual similarity to retrieve historical fixes, which often fails to accurately capture cycle-accurate hardware timing semantics, thereby limiting repair effectiveness. This work proposes MechMem-RTL, a novel framework that introduces “mechanistic memory”—formally verified knowledge units—into the LLM-driven repair pipeline. Each memory entry encapsulates triggering evidence, fault mechanism, repair action, constraints, and verification summary, and is reused only when a new fault strictly matches the stored triggering conditions. By grounding knowledge reuse in deterministic formal evidence rather than natural language descriptions, MechMem-RTL overcomes key limitations of conventional retrieval-augmented generation (RAG). Evaluated on 48 public timing-sensitive RTL tasks, MechMem-RTL successfully repairs 180 out of 288 task–model combinations within at most two attempts, substantially outperforming standard feedback-based repair (109) and task-similarity RAG (107).

Abstract

Large language models (LLMs) can automatically repair register-transfer-level (RTL) designs. However, fixing complex sequential logic errors requires reusing past debugging experience. Existing retrieval-augmented generation (RAG) relies on task-text similarity to provide this experience. This text-based approach often misguides the model because natural language poorly reflects cycle-level hardware execution semantics. To address this, we present MechMem-RTL, a repair framework that reuses verifier-confirmed repair records instead of text similarity. Each stored record strictly links trigger evidence, a diagnosed failure mechanism, a local repair action, preservation constraints, and a verification summary. For a new failure, MechMem-RTL injects a past record only when deterministic verifier evidence is strictly compatible with the stored trigger. Otherwise, the system uses only current verifier evidence. We evaluate MechMem-RTL on 48 public sequential RTL tasks across six repair models. With at most two repair attempts per task, MechMem-RTL successfully resolves 180 out of 288 task-model pairs, outperforming standard feedback repair (109 pairs) and task-similarity RAG (107 pairs).

Problem

• RTL repair
• sequential logic errors
• retrieval-augmented generation
• hardware execution semantics
• large language models

Innovation

• MechMem-RTL
• retrieval-augmented generation
• verifier-confirmed repair records
• sequential RTL repair
• hardware execution semantics