How Large Language Models Are Quietly Changing RTL Design?
RTL design has always been one of the most crucial – and complex – steps in digital chip development. It’s where an engineer takes a specification and expresses it in hardware behaviour using languages like Verilog or System Verilog. As chips become more advanced – especially at 2 nm and below – the design complexity grows, timelines shrink, and the demand for precision increases. While synthesis and verification tools have improved, RTL development still relies heavily on human coding and review. But in the background, a new kind of tool is starting to help large language models, like those based on the GPT architecture.
These models, originally trained to understand and generate natural language, are now being fine-tuned on hardware design data. That means they can “understand” Verilog, recognize control logic patterns, and even help generate hardware code from textual descriptions. This isn’t just autocompleted with a wider vocabulary – it’s a shift in how engineers can approach their work.
For example, when given a design prompt like “Create a 3-stage pipelined multiplier with enable control,” a GPT-based model can generate a correct Verilog module that includes pipeline registers, enable signals, and control logic. It may even include synthesis-friendly structures and reasonable defaults. This initial output might not be perfect, but it’s a working draft – saving hours of manual coding and letting the engineer focus on correctness, constraints, and optimization.
The potential doesn’t stop at code generation. LLMs are now being integrated into early RTL design environments where they assist in debugging, waveform analysis, and even testbench generation. An engineer might ask, “Why did the valid signal go low after cycle 10?” and the model could trace back through the logic, point to likely causes, or recommend additional assertions. This is possible because LLMs can now work with contextual information: simulation logs, timing reports, and even architectural specifications in PDF form.
Some design teams are moving toward agentic design environments – were multiple specialized AI agents, powered by LLMs, handle tasks collaboratively. One agent interprets the spec, another generates RTL, a third produces System Verilog testbenches, and another checks for coverage gaps. These agents don’t replace the engineer; they accelerate the flow, catch common errors, and help reduce turnaround time.
Naturally, there are limits. GPT-style models don’t have a built-in understanding of silicon physics, nor can they guarantee correctness across timing corners or voltage domains. Their outputs must be treated with caution – verified through formal methods and validated in simulation. Hallucinations – confident but incorrect responses – are still a known issue. That’s why most teams treat LLMs as co-pilots, not as fully autonomous designers. They’re there to assist, not replace.
Where these models shine most is in removing friction. They can help new engineers ramp up quickly, explain the purpose of logic blocks, and assist in formatting design documentation. In educational settings, they’re already being used to walk through state machine design or clock domain crossing scenarios. And in professional teams, they’re helping designers explore architectures faster – creating quick prototypes that can be stress-tested early in the process.
At BITSILICA, we believe this technology marks a shift in how RTL design will be done in the coming years. It’s not about turning design over to AI; it’s about creating tools that understand enough context to help designers move faster and make fewer mistakes. We’re actively building workflows that use large language models as part of a closed-loop system – where AI-generated RTL is verified, tested, and improved continuously.
The future of chip design won’t be built by machines alone – but it will be built faster, better, and more creatively when engineers have intelligent tools by their side. In that future, large language models like GPT aren’t just generating text. They’re co-authoring the next generation of silicon.
