Summary of Verilogeval: Evaluating Large Language Models For Verilog Code Generation, by Mingjie Liu et al.
VerilogEval: Evaluating Large Language Models for Verilog Code Generation
by Mingjie Liu, Nathaniel Pinckney, Brucek Khailany, Haoxing Ren
First submitted to arxiv on: 14 Sep 2023
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Software Engineering (cs.SE)
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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 The proposed benchmarking framework evaluates large language model (LLM) performance in generating Verilog code for hardware design and verification. A comprehensive evaluation dataset is presented, consisting of 156 problems from the HDLBits instructional website. The evaluation set covers a range of tasks, from simple combinational circuits to complex finite state machines. Functional correctness can be tested by comparing simulation outputs with golden solutions. Additionally, LLMs’ Verilog code generation capabilities are improved through supervised fine-tuning using synthetic problem-code pairs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers created a special way to test how well computers can generate code for designing and testing electronic circuits. They made a big list of 156 problems from an online website that teaches Verilog programming. These problems cover different types of circuits, like simple combinations or more complex machines with many states. To see if the generated code works correctly, they compare it to a known correct version. They also showed that by teaching computers some examples, they can get better at generating code for these circuits. |
Keywords
* Artificial intelligence * Fine tuning * Large language model * Supervised
