Researchers have developed RuC, a new framework for generating hardware description language (HDL) code completion benchmarks. This system is grammar-driven and language-agnostic, allowing for controlled evaluation of Large Language Models (LLMs) in Register Transfer Level (RTL) development. RuC masks code regions based on HDL grammar and prompts models to regenerate them, enabling assessment of capabilities from simple assignments to entire logic blocks. A study using RuC on SystemVerilog benchmarks from Tiny Tapeout and a RISC-V core showed that completion performance is influenced by model type, masked region structure, and prompting strategy, with Fill-in-the-Middle (FIM) yielding the best results. AI
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IMPACT Provides a more granular and controlled method for evaluating LLMs in RTL development, potentially improving model performance for hardware design tasks.
RANK_REASON Academic paper introducing a new benchmark generation framework for LLMs in hardware description languages.