arXiv AI

Large Language Model for Verilog Code Generation: Literature Review and the Road Ahead

The article reviews the growing use of Large Language Models (LLMs) for generating Verilog code, a key hardware description language in electronic design automation. It surveys 102 papers, covering conferences, journals, and preprints, and addresses four research questions about LLM selection, datasets, techniques, and alignment strategies. The review identifies current limitations and proposes a roadmap for future research in LLM-assisted hardware design.

arXiv Machine Learning
Jul 28

Benchmarking LLMs for Verilog Design Flows

arXiv:2607. 22759v1 Announce Type: cross Abstract: Large language models (LLMs) show promise in code generation, but their capabilities to produce correct, synthesizable hardware description language (HDL) code still remain to be properly benchmarked.

By Angshuman Chakravertty, Rahul Koshti, Buddhi Prakash Sharma, Vinay Chamola
arXiv AI
Jun 4

StepPRM-RTL: Stepwise Process-Reward Guided LLM Fine-Tuning for Enhanced RTL Synthesis

arXiv:2606. 04246v1 Announce Type: new Abstract: Automatic generation of RTL code for digital hardware designs remains challenging due to long-horizon reasoning, multi-step dependencies, and strict correctness constraints in Verilog and VHDL.

By Prashanth Vijayaraghavan, Apoorva Nitsure, Luyao Shi, Ehsan Degan, Vandana Mukherjee