How LLMs Fail and Generalize in RTL Coding for Hardware Design?
For hardware designers using LLMs, the paper identifies fundamental limitations in current models' ability to handle parallel temporal logic, showing that pretraining knowledge bounds performance.
The paper introduces a taxonomy for LLM errors in RTL coding, finding that frontier models plateau at a 90.8% pass rate on VerilogEval due to unsolvable functional errors, and that alignment techniques only teach models to compile without improving reasoning.
Translating sequential programming priors into the parallel temporal logic of hardware design remains a crucial bottleneck for large language models(LLM). To investigate this, we introduce a new error taxonomy grounded in problem solvability, inspired by cognitive theory. Our taxonomy categorizes failures into syntactic, semantic, solvable functional, and unsolvable functional types. Evaluations reveal a strict empirical ceiling on the VerilogEval benchmark, as frontier models plateau at a 90.8% initial pass rate. These plateaus are defined by unsolvable functional errors, exposing persistent knowledge gaps immune to test time compute scaling. Furthermore, we expose a striking surface convergence gap: optimization readily eliminates syntax errors but concurrently exacerbates deeper functional failures. Our findings demonstrate that alignment techniques merely teach models to compile. While repeated sampling strategies can patch solvable errors, register-transfer level(RTL) coding capacity remains strictly bounded by pretraining knowledge. Addressing challenges in the current LLM based hardware generation pipeline requires more studies in model reasoning rather than alignment interventions.