9 papers
AutoPPA: Automated Circuit PPA Optimization via Contrastive Code-based Rule Library Learning
Chongxiao Li, Pengwei Jin, Di Huang +14
Performance, power, and area (PPA) optimization is a fundamental task in RTL design, requiring a precise understanding of circuit functionality and the relationship between circuit…
QiMeng-CRUX: Narrowing the Gap Between Natural Language and Verilog via Core Refined Understanding eXpression for Circuit Design
Lei Huang, Rui Zhang, Jiaming Guo +9
Large language models (LLMs) have shown promising capabilities in hardware description language (HDL) generation. However, existing approaches often rely on free-form natural langu…
QiMeng-CodeV-R1: Reasoning-Enhanced Verilog Generation
Yaoyu Zhu, Di Huang, Hanqi Lyu +16
Large language models (LLMs) trained via reinforcement learning with verifiable reward (RLVR) have achieved breakthroughs on tasks with explicit, automatable verification, such as…
LocalV: Exploiting Information Locality for IP-level Verilog Generation
Hanqi Lyu, Di Huang, Yaoyu Zhu +10
The generation of Register-Transfer Level (RTL) code is a crucial yet labor-intensive step in digital hardware design, traditionally requiring engineers to manually translate compl…
QiMeng-SALV: Signal-Aware Learning for Verilog Code Generation
Yang Zhang, Rui Zhang, Jiaming Guo +10
The remarkable progress of Large Language Models (LLMs) presents promising opportunities for Verilog code generation which is significantly important for automated circuit design.…
RealBench: Benchmarking Verilog Generation Models with Real-World IP Designs
Pengwei Jin, Di Huang, Chongxiao Li +10
The automatic generation of Verilog code using Large Language Models (LLMs) has garnered significant interest in hardware design automation. However, existing benchmarks for evalua…