These labels were automatically added by AI and may be inaccurate. For details, see About Literature Database.
Abstract
Recent advances of large language models in the field of Verilog generation
have raised several ethical and security concerns, such as code copyright
protection and dissemination of malicious code. Researchers have employed
watermarking techniques to identify codes generated by large language models.
However, the existing watermarking works fail to protect RTL code copyright due
to the significant syntactic and semantic differences between RTL code and
software code in languages such as Python. This paper proposes a hardware
watermarking framework RTLMarker that embeds watermarks into RTL code and
deeper into the synthesized netlist. We propose a set of rule-based Verilog
code transformations , ensuring the watermarked RTL code's syntactic and
semantic correctness. In addition, we consider an inherent tradeoff between
watermark transparency and watermark effectiveness and jointly optimize them.
The results demonstrate RTLMarker's superiority over the baseline in RTL code
watermarking.