MeltRTL: Multi-Expert LLMs with Inference-time Intervention for RTL Code Generation

The automated generation of hardware register-transfer level (RTL) code with large language models (LLMs) shows promise, yet current solutions struggle to produce syntactically and functionally correct code for complex digital designs. This paper introduces MeltRTL, a novel framework that integrates multi-expert attention with inference-time intervention (ITI) to significantly improve LLM-based RTL code generation accuracy without retraining the base model. MeltRTL introduces three key innovations: (1) A multi-expert attention architecture that dynamically routes design specifications to specialized expert networks, enabling targeted reasoning across various hardware categories; (2) An inference-time intervention mechanism that employs non-linear probes to detect and correct hardware-specific inaccuracies during generation; and (3) An efficient intervention framework that selectively operates on expert-specific attention heads with minimal computational overhead. We evaluate MeltRTL on the VerilogEval benchmark, achieving 96% synthesizability and 60% functional correctness, compared to the base LLM's 85.3% and 45.3%, respectively. These improvements are obtained entirely at inference time, with only 27% computational overhead and no model fine-tuning, making MeltRTL immediately deployable on existing pre-trained LLMs. Ablation studies further show the complementary benefits of multi-expert architecture and ITI, highlighting their synergistic effects when combined.