arXiv Machine Learning

QiMeng-ChipV-RTL: Exploiting Information Locality for IP-level Verilog Generation

QiMeng-ChipV-RTL is a multi‑agent framework that tackles the challenges of generating Register‑Transfer Level (RTL) code for industrial IP‑level hardware design. By partitioning long design documents into short, localized tasks and using hierarchical planning, localized code generation, interface‑consistent merging, and AST‑guided debugging, it scales to complex specifications. Experiments on the RealBench benchmark show ChipV-RTL achieves a 45.0% pass rate, outperforming state‑of‑the‑art LLMs and agents which reach only 21.6%.

arXiv AI
Aug 20

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.

By Guang Yang, Wei Zheng, Xiang Chen, Dong Liang, Peng Hu, Yukui Yang, Shaohang Peng, Zhenghan Li, Jiahui Feng, Xiao Wei, Kexin Sun, Deyuan Ma, Haotian Cheng, Yiheng Shen, Xing Hu, Terry Yue Zhuo, David Lo
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
Jul 21

CLOSER-Bench: Evaluating Budgeted Cross-Stage Design Closure for Hardware Agents

arXiv:2607. 16632v1 Announce Type: cross Abstract: Hardware engineering exposes coding agents to a form of long-horizon work that is difficult to capture with pass-at-k: progress is continuous, tool feedback is delayed and heterogeneous, and a backend failure may require revising RTL rather than tuning another physical-design parameter.

By Peilong Zhou, Zhirong Chen, Cangyuan Li, Haoyu Gao, Kaiyan Chang, Ziming Qu, Ying Wang
arXiv AI
Aug 10

HLSmith: An Expert-Guided Agentic Framework for C/C++-to-HLS Translation

arXiv:2608. 06791v1 Announce Type: cross Abstract: Application-specific FPGA accelerators offer substantial performance and energy-efficiency gains across many application domains, but developing them is costly, often requiring months of specialized effort.

By Yuebo Luo, Ahmad Sedigh Baroughi, Philip Stachura, Le Chen, Venkatram Vishwanath, Zhenman Fang, Caiwen Ding
arXiv AI
2d ago

Benchmarking AI Agents for Hardware Design Automation via MCP Tool Calling

The paper investigates whether locally deployed large language models can automate hardware design workflows that involve repetitive, dependency-ordered operations using specialized tools. A Model Context Protocol (MCP) server is created to emulate a proprietary hardware design tool, and a benchmark tests single and multi-step edits, invalid requests, misspelled prompts, and multi-server contexts. Seven open-source models are evaluated across different pipeline choices, revealing that strong models can nearly fully cover expected calls, but reliability hinges on task structure and agent configuration, with comprehensive tool descriptions reducing failures and multi-agent setups aiding weaker models at the cost of extra calls.

By Leonardo Liparulo, Francesco Pierri