5 papers
DynamicRTL: RTL Representation Learning for Dynamic Circuit Behavior
Ruiyang Ma, Yunhao Zhou, Yipeng Wang +9
There is a growing body of work on using Graph Neural Networks (GNNs) to learn representations of circuits, focusing primarily on their static characteristics. However, these model…
DeepRTL2: A Versatile Model for RTL-Related Tasks
Yi Liu, Hongji Zhang, Yunhao Zhou +3
The integration of large language models (LLMs) into electronic design automation (EDA) has significantly advanced the field, offering transformative benefits, particularly in regi…
Speculative Decoding for Verilog: Speed and Quality, All in One
Changran Xu, Yi Liu, Yunhao Zhou +3
The rapid advancement of large language models (LLMs) has revolutionized code generation tasks across various programming languages. However, the unique characteristics of programm…
DeepCircuitX: A Comprehensive Repository-Level Dataset for RTL Code Understanding, Generation, and PPA Analysis
Zeju Li, Changran Xu, Zhengyuan Shi +11
This paper introduces DeepCircuitX, a comprehensive repository-level dataset designed to advance RTL (Register Transfer Level) code understanding, generation, and power-performance…
DeepRTL: Bridging Verilog Understanding and Generation with a Unified Representation Model
Yi Liu, Changran Xu, Yunhao Zhou +2
Recent advancements in large language models (LLMs) have shown significant potential for automating hardware description language (HDL) code generation from high-level natural lang…