Marco Framework for Configurable Graph-Based Task Solving and Multi-AI Agents
Introduction
Chip and hardware design present numerous challenges due to their complexity and advancing technologies. These challenges result in longer turn-around time (TAT) for optimizing performance, power, area, and cost (PPAC) during synthesis, verification, physical design, and reliability loops.
Marco Framework Overview
The Marco framework is a configurable graph-based task solving and multi-AI agents framework that encompasses graph-based task solving, agent configurations for sub-tasks, and skill/tool configurations for each AI agent in real-time. The framework is designed to flexibly integrate chip-design knowledge (e.g., circuits, timing, etc.) with dynamic and static configurable graph-based task solving.
Task Graph and Agent Configurations
The task graph represents each node as a sub-task, and each edge represents the execution or knowledge relationship between nodes. To solve each sub-task, the Marco framework configures single-AI or multi-AI agents with a knowledge database, tools, and memory. Table 1 summarizes the task graph, agent, and LLM configurations for various agents.
Automated Hardware Description Languages Code Generation
One key area where autonomous agents are making an impact is in the generation of hardware description languages (HDLs), such as Verilog. LLMs can generate Verilog code from natural language descriptions, but often struggle to produce code that is both syntactically and functionally correct. The Marco framework addresses this challenge through the use of a combination of retrieval-augmented generation (RAG) and ReAct prompting to enable LLMs to iteratively debug and fix syntax errors.
DRC Layout Code Generation
DRC-Coder uses multiple autonomous agents with vision capabilities and specialized DRC and Layout DRV analysis tools to generate DRC code. The system interprets design rules from textual descriptions, visual illustrations, and layout representations.
Standard Cell Layout Optimization
LLM agents can also be used for standard cell layout optimization by generating high-quality cluster constraints incrementally to optimize cell layout PPA and debug routability with ReAct prompting.
Multi-Corner Multi-Mode Timing Report Debug and Analysis
The multi-corner multi-mode (MCMM) timing analysis agent uses dynamic task graphs to complete the specification-to-RTL and extract key takeaways of timing reports, respectively.
Conclusion
The proposed Marco framework enables more flexible and domain-specialized methods for real-time hardware design tasks solving. By using task graph and flexible single-AI and multi-AI agent configurations with domain-specific tools and knowledge, we developed various agents for tasks such as cell layout optimization, Verilog syntax error fixing, Verilog and DRC code generation, and timing debugging on problematic blocks, nets, and wires.
FAQs
Q: What is the Marco framework?
A: The Marco framework is a configurable graph-based task solving and multi-AI agents framework that enables more flexible and domain-specialized methods for real-time hardware design tasks solving.
Q: What are the key benefits of the Marco framework?
A: The key benefits of the Marco framework include faster product cycles, lower costs, improved design reliability, and reduced risk of costly errors.
Q: What are the future directions for agent research on hardware design?
A: The future directions for agent research on hardware design include training LLMs with high-quality hardware design data, improving LLM-based agents’ ability for hardware signal and waveform debugging, incorporating PPA metrics into the design flow, and developing more efficient self-learning techniques and memory systems for LLM agents for solving more complex hardware tasks.
