Multi-Modality and Multi-AI Agents for Hardware Design

Hardware design presents numerous challenges due to its complexity and rapidly advancing technologies. These challenges result in longer turnaround times (TAT) for optimizing performance, power, area, and cost (PPAC) during synthesis, verification, physical design, and reliability loops. Large language models have demonstrated a remarkable ability to comprehend and generate natural language at scale, leading to numerous potential applications and benefits across various domains. Successful LLM-based agents for hardware design can significantly reduce TAT, enabling faster product cycles, lower costs, improved design reliability, and a reduced risk of costly errors. Cutting-edge multi-AI agents have shown promising results in Hardware Description Language (HDL) generation, layout optimization, design rule code generation, and Multi-Corner Multi-Mode (MCMM) timing report analysis and debugging at both the block and path levels. Notably, the developed multi-AI agents not only achieve a 94.2% functionally correct pass rate on the VerilogEval-Human v2 benchmark, but also deliver a 60X speedup in MCMM timing report analysis compared to experienced human engineers.