MEIC: Re-thinking RTL Debug Automation using LLMs

• URL: http://arxiv.org/abs/2405.06840v1
• Date: Fri, 10 May 2024 22:32:39 GMT
• Title: MEIC: Re-thinking RTL Debug Automation using LLMs
• Authors: Ke Xu, Jialin Sun, Yuchen Hu, Xinwei Fang, Weiwei Shan, Xi Wang, Zhe Jiang,
• Abstract summary: This work introduces a novel framework, Make Each Iteration Count (MEIC) MEIC is suitable for identifying and correcting both syntax and function errors. To evaluate our framework, we provide an open-source dataset comprising 178 common RTL programming errors.
• Score: 18.964523115622928
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The deployment of Large Language Models (LLMs) for code debugging (e.g., C and Python) is widespread, benefiting from their ability to understand and interpret intricate concepts. However, in the semiconductor industry, utilising LLMs to debug Register Transfer Level (RTL) code is still insufficient, largely due to the underrepresentation of RTL-specific data in training sets. This work introduces a novel framework, Make Each Iteration Count (MEIC), which contrasts with traditional one-shot LLM-based debugging methods that heavily rely on prompt engineering, model tuning, and model training. MEIC utilises LLMs in an iterative process to overcome the limitation of LLMs in RTL code debugging, which is suitable for identifying and correcting both syntax and function errors, while effectively managing the uncertainties inherent in LLM operations. To evaluate our framework, we provide an open-source dataset comprising 178 common RTL programming errors. The experimental results demonstrate that the proposed debugging framework achieves fix rate of 93% for syntax errors and 78% for function errors, with up to 48x speedup in debugging processes when compared with experienced engineers. The Repo. of dataset and code: https://anonymous.4open.science/r/Verilog-Auto-Debug-6E7F/.

Related papers

• SpecTool: A Benchmark for Characterizing Errors in Tool-Use LLMs [77.79172008184415]
  SpecTool is a new benchmark to identify error patterns in LLM output on tool-use tasks. We show that even the most prominent LLMs exhibit these error patterns in their outputs. Researchers can use the analysis and insights from SPECTOOL to guide their error mitigation strategies.
  arXiv  Detail & Related papers  (2024-11-20T18:56:22Z)
• TRANSAGENT: An LLM-Based Multi-Agent System for Code Translation [16.46292795782835]
  Code translation is crucial for software migration, system ablation, and cross-platform development. Traditional rule-based methods rely on manually-written rules, which can be time-consuming and often result in less readable code. More recently, the advance of Large Language Models (LLMs) further boosts learning-based code translation. We propose a novel multi-agent system TRANSAGENT, which enhances LLM-based code translation by fixing the syntax errors and semantic errors.
  arXiv  Detail & Related papers  (2024-09-30T02:53:03Z)
• BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions [72.56339136017759]
  We introduce BigCodeBench, a benchmark that challenges Large Language Models (LLMs) to invoke multiple function calls as tools from 139 libraries and 7 domains for 1,140 fine-grained tasks. Our evaluation shows that LLMs are not yet capable of following complex instructions to use function calls precisely, with scores up to 60%, significantly lower than the human performance of 97%. We propose a natural-language-oriented variant of BigCodeBench, BigCodeBench-Instruct, that automatically transforms the original docstrings into short instructions only with essential information.
  arXiv  Detail & Related papers  (2024-06-22T15:52:04Z)
• Are you still on track!? Catching LLM Task Drift with Activations [55.75645403965326]
  Task drift allows attackers to exfiltrate data or influence the LLM's output for other users. We show that a simple linear classifier can detect drift with near-perfect ROC AUC on an out-of-distribution test set. We observe that this approach generalizes surprisingly well to unseen task domains, such as prompt injections, jailbreaks, and malicious instructions.
  arXiv  Detail & Related papers  (2024-06-02T16:53:21Z)
• Aligning LLMs for FL-free Program Repair [14.935596175148586]
  This paper investigates a new approach to adapt large language models (LLMs) to program repair. Our core insight is that LLM's APR capability can be greatly improved by simply aligning the output to their training objective. Based on this insight, we designed D4C, a straightforward prompting framework for APR.
  arXiv  Detail & Related papers  (2024-04-13T02:36:40Z)
• CodecLM: Aligning Language Models with Tailored Synthetic Data [51.59223474427153]
  We introduce CodecLM, a framework for adaptively generating high-quality synthetic data for instruction-following abilities. We first encode seed instructions into metadata, which are concise keywords generated on-the-fly to capture the target instruction distribution. We also introduce Self-Rubrics and Contrastive Filtering during decoding to tailor data-efficient samples.
  arXiv  Detail & Related papers  (2024-04-08T21:15:36Z)
• ML-Bench: Evaluating Large Language Models and Agents for Machine Learning Tasks on Repository-Level Code [76.84199699772903]
  ML-Bench is a benchmark rooted in real-world programming applications that leverage existing code repositories to perform tasks. To evaluate both Large Language Models (LLMs) and AI agents, two setups are employed: ML-LLM-Bench for assessing LLMs' text-to-code conversion within a predefined deployment environment, and ML-Agent-Bench for testing autonomous agents in an end-to-end task execution within a Linux sandbox environment.
  arXiv  Detail & Related papers  (2023-11-16T12:03:21Z)
• Evaluating Diverse Large Language Models for Automatic and General Bug Reproduction [12.851941377433285]
  Large language models (LLMs) have been demonstrated to be adept at natural language processing and code generation. Our proposed technique LIBRO could successfully reproduce about one-third of all bugs in the widely used Defects4J benchmark.
  arXiv  Detail & Related papers  (2023-11-08T08:42:30Z)
• Assessing the Reliability of Large Language Model Knowledge [78.38870272050106]
  Large language models (LLMs) have been treated as knowledge bases due to their strong performance in knowledge probing tasks. How do we evaluate the capabilities of LLMs to consistently produce factually correct answers? We propose MOdel kNowledge relIabiliTy scORe (MONITOR), a novel metric designed to directly measure LLMs' factual reliability.
  arXiv  Detail & Related papers  (2023-10-15T12:40:30Z)
• Benchmarking Large Language Models for Automated Verilog RTL Code Generation [21.747037230069854]
  We characterize the ability of large language models (LLMs) to generate useful Verilog. We construct an evaluation framework comprising test-benches for functional analysis and a flow to test the syntax of Verilog code. Our findings show that across our problem scenarios, the fine-tuning results in LLMs more capable of producing syntactically correct code.
  arXiv  Detail & Related papers  (2022-12-13T16:34:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.