RTL-BenchLS: A Large-Scale Benchmark for RTL Reasoning and Generation with Large Language Models
arXiv:2606.08976v1 Announce Type: new Abstract: LLM-based RTL generation and reasoning is a promising direction for hardware design automation. High-quality benchmarks are critical infrastructure for tracking progress in this direction. However, existing RTL benchmarks face inherent limitations in both scale and task scope. The designs they cover are typically small and simple, and the tasks focus almost entirely on specification-to-RTL generation. Frontier models' performance already saturates on the existing benchmarks. Scaling these benchmarks up is fundamentally difficult because aligned l
The rapid advancement of Large Language Models (LLMs) is pushing the boundaries of AI applications, making the integration of LLMs into hardware design a natural next step for efficiency and complexity management.
This development indicates a significant push towards automating complex hardware design, potentially accelerating the development of next-generation chips and reducing human intervention in a critical technological domain.
Existing benchmarks for RTL generation are becoming obsolete, and a new, larger-scale benchmark signifies a maturation in the field, enabling more accurate tracking of LLM performance in hardware design automation.
- · AI model developers
- · Semiconductor companies
- · Hardware design engineers
- · EDA tool vendors
- · Manual RTL design processes
- · Companies reliant on outdated design methodologies
Introduction of a large-scale, high-quality benchmark accelerates research and development in LLM-based RTL generation and reasoning.
Improved automation in hardware design leads to faster iteration cycles and potentially more complex and efficient chip architectures.
The reduced barrier to entry for hardware design through AI automation could democratize chip development, fostering innovation and competition globally.
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Read at arXiv cs.AI