Presentation
Automating RTL generation using Agentic LLMs
DescriptionWe explore the application of agentic Large Language Models (LLMs) for HW design. Generation and optimization of Register-Transfer Level (RTL) code is a challenging task and requires significant human effort. Agentic flow consisting of multiple agents, which are a combination of specialized LLMs and hardware simulation tools, can work together to complete the complex task of hardware design. This flow may incorporate human feedback in order to improve the efficiency of these tools. The proposed agentic flow, built on the open-source AutoGen framework, leverages iterative error feedback to significantly improve efficiency. This approach improves test pass rates and ensures successful compilation in RTL code generation, all while keeping computational overhead minimal. Additionally, it streamlines high-level design specifications, guaranteeing syntactical accuracy, compilation reliability, and functional integrity of the generated RTL. A key feature of this flow is its self-correcting mechanism, where outputs from each stage are refined through iterative feedback loops and validated against test benches treated as black boxes. The study also investigates the trade-offs between open-source and closed-source LLMs as primary code generators in a zero-shot setting. To validate this adaptive approach to code generation, benchmarking is performed using two open-source natural language-to-RTL datasets. To the best of our knowledge, this is the first work to explore the feasibility of agentic flows for RTL generation.
Event Type
Networking
Work-in-Progress Poster
TimeSunday, June 226:00pm - 7:00pm PDT
LocationLevel 3 Lobby