arXiv AI August 11, 2026 By Deeksha Nandal, Riccardo Revalor, Soham Dan, Debjit Pal

LAUDE: LLM-Assisted Unit Test Generation and Debugging of Hardware DEsigns

arXiv:2601. 08856v3 Announce Type: replace-cross Abstract: Unit tests are critical in the hardware design lifecycle to ensure that component design modules are functionally correct and conform to the specification before they are integrated at the system level.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

llms

arXiv AI

Jun 24

VeriPilot: An LLM-Powered Verilog Debugging Framework

arXiv:2606. 23759v1 Announce Type: cross Abstract: Verilog debugging remains one of the most time-consuming stages in digital circuit design.

By Yihan Wang, Cheng Liu, Jiazheng Zhang, Lei Zhang, Long Cheng, Xiaowei Li, Huawei Li

llms benchmarks

arXiv AI

Jun 15

VHDLSuite: Unified Pipeline for LLM VHDL Generation with Data Synthesis and Evaluation

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.

By Yijun Shen, Minghao Shao, Yichen Zhao, Zhuoyan Yu, Boyuan Chen, Yik-Cheung Tam, Muhammad Shafique

llms benchmarks data-engineering

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

llms benchmarks

arXiv AI

Jun 9

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.

By Jing Wang, Shang Liu, Wenji Fang, Yuchao Wu, Yugao Zhu, Zhiyao Xie

llms benchmarks

arXiv AI

Jun 19

Interpretable and Verifiable Hardware Generation with LLM-Driven Stepwise Refinement

arXiv:2606. 19387v1 Announce Type: cross Abstract: Large language models (LLMs) have achieved remarkable success in software development.

By You Li, Samuel Mandell, David Z. Pan

llms agents safety

arXiv AI

Jun 16

LLM4RTL: Tool-Assisted LLM for RTL Generation

arXiv:2606. 15500v1 Announce Type: cross Abstract: Large language models (LLMs) have facilitated impressive progress in software engineering, code generation, tooling, and systems.

By Jing Jin, Robert Chu, Ning Yan, Masood S. Mortazavi

llms fine-tuning benchmarks

Related stories

VeriPilot: An LLM-Powered Verilog Debugging Framework

VHDLSuite: Unified Pipeline for LLM VHDL Generation with Data Synthesis and Evaluation

Benchmarking LLMs for Verilog Design Flows

RTL-BenchLS: A Large-Scale Benchmark for RTL Reasoning and Generation with Large Language Models

Interpretable and Verifiable Hardware Generation with LLM-Driven Stepwise Refinement

LLM4RTL: Tool-Assisted LLM for RTL Generation