Presentation
Beyond Verilog: Agents for Emerging HDLs
DescriptionLarge Language Models (LLMs) are transforming the programming language
landscape by facilitating learning for beginners, enabling code generation, and
optimizing documentation workflows. Hardware Description Languages (HDLs),
with their smaller user community, stand to benefit significantly from the
application of LLMs as tools for learning new HDLs. This paper investigates the
challenges and solutions of enabling LLMs for HDLs, particularly for
HDLs that LLMs have not been previously trained on.
This work introduces HDLAgent, an AI agent optimized for LLMs with limited
knowledge of various HDLs. It significantly enhances off-the-shelf LLMs. For
example, PyRTL's success rate improves from zero to 35\% with Mixtral 8x7B, and
Chisel's success rate increases from zero to 59\% with GPT-3.5-turbo-0125.
HDLAgent offers an LLM-neutral framework to accelerate the adoption and growth
of HDL user bases in the era of LLMs.
landscape by facilitating learning for beginners, enabling code generation, and
optimizing documentation workflows. Hardware Description Languages (HDLs),
with their smaller user community, stand to benefit significantly from the
application of LLMs as tools for learning new HDLs. This paper investigates the
challenges and solutions of enabling LLMs for HDLs, particularly for
HDLs that LLMs have not been previously trained on.
This work introduces HDLAgent, an AI agent optimized for LLMs with limited
knowledge of various HDLs. It significantly enhances off-the-shelf LLMs. For
example, PyRTL's success rate improves from zero to 35\% with Mixtral 8x7B, and
Chisel's success rate increases from zero to 59\% with GPT-3.5-turbo-0125.
HDLAgent offers an LLM-neutral framework to accelerate the adoption and growth
of HDL user bases in the era of LLMs.
Event Type
Networking
Work-in-Progress Poster
TimeSunday, June 226:00pm - 7:00pm PDT
LocationLevel 3 Lobby