Summary of Verilogcoder: Autonomous Verilog Coding Agents with Graph-based Planning and Abstract Syntax Tree (ast)-based Waveform Tracing Tool, by Chia-tung Ho et al.
VerilogCoder: Autonomous Verilog Coding Agents with Graph-based Planning and Abstract Syntax Tree (AST)-based Waveform Tracing Tool
by Chia-Tung Ho, Haoxing Ren, Brucek Khailany
First submitted to arxiv on: 15 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research proposes a system called VerilogCoder that uses artificial intelligence (AI) agents to generate and fix errors in Verilog code for designing digital systems. The system consists of multiple AI agents working together to autonomously write Verilog code, using collaborative tools such as syntax checkers, simulators, and waveform tracers. A task planner is developed to construct a holistic plan based on module descriptions, while an abstract syntax tree (AST)-based waveform tracing tool is used to debug and fix functional errors. The proposed methodology outperforms state-of-the-art methods by 33.9% on the VerilogEval-Human v2 benchmark, successfully generating 94.2% syntactically and functionally correct Verilog code. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary VerilogCoder is a system that helps design digital systems by writing Verilog code. It uses artificial intelligence to fix errors in the code and make it work correctly. The system has different parts that work together to do this, like a task planner and an error-fixer tool. This helps engineers design better systems with fewer mistakes. |
Keywords
» Artificial intelligence » Syntax
