arXiv:2606.13735v1 Announce Type: cross Abstract: Large Language Models (LLM) have shown impressive capabilities in Register Transfer Level (RTL) code generation, particularly for Verilog. However, evaluating their performance with other Hardware Description Languages (HDL), especially VHDL, remains limited although its distinct language characteristics, such as stricter semantic rules, introduce evaluation considerations that differ from Verilog. This lack of coverage restricts fully understanding of how well current models generalize across hardware design languages with differing structures
The proliferation of LLMs and increasing demand for efficient hardware design necessitate advanced tools for RTL code generation and verification, particularly for less-explored languages like VHDL.
Improving LLM capabilities in VHDL generation streamlines hardware design, accelerating chip development and potentially diversifying the global semiconductor supply chain beyond dominant architectures.
The ability to reliably generate and evaluate VHDL code using LLMs begins to bridge the gap between AI and complex hardware description languages, setting a precedent for broader application of LLMs in specialized engineering domains.
- · Semiconductor design companies
- · Hardware engineers
- · EDA tool developers
- · Traditional HDL code generation methods
- · Companies slow to adopt AI-powered design
Increased efficiency and reduced error rates in VHDL-based hardware design will become common.
The cost and time-to-market for complex VHDL-based chips will decrease, fostering innovation in specialized hardware.
Enhanced toolchains for VHDL could potentially democratize hardware design, making it accessible to a broader range of developers and contributing to more diverse hardware ecosystems.
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Read at arXiv cs.AI