Wrong Code, Right Structure: Learning Netlist Representations from Imperfect LLM-Generated RTL
arXiv:2603.09161v1 Announce Type: new Abstract: Learning effective netlist representations is fundamentally constrained by the scarcity of labeled datasets, as real designs are protected by Intellectual Property (IP) and costly to annotate. Existing work therefore focuses on small-scale circuits with clean labels, limiting scalability to realistic designs. Meanwhile, Large Language Models (LLMs) can generate Register-Transfer-Level (RTL) at scale, but their functional incorrectness has hindered their use in circuit analysis. In this work, we make a key observation: even when LLM-Generated RTL is functionally imperfect, the synthesized netlists still preserve structural patterns that are strongly indicative of the intended functionality. Building on this insight, we propose a cost-effective data augmentation and training framework that systematically exploits imperfect LLM-Generated RTL as training data for netlist representation learning, forming an end-to-end pipeline from automated
arXiv:2603.09161v1 Announce Type: new Abstract: Learning effective netlist representations is fundamentally constrained by the scarcity of labeled datasets, as real designs are protected by Intellectual Property (IP) and costly to annotate. Existing work therefore focuses on small-scale circuits with clean labels, limiting scalability to realistic designs. Meanwhile, Large Language Models (LLMs) can generate Register-Transfer-Level (RTL) at scale, but their functional incorrectness has hindered their use in circuit analysis. In this work, we make a key observation: even when LLM-Generated RTL is functionally imperfect, the synthesized netlists still preserve structural patterns that are strongly indicative of the intended functionality. Building on this insight, we propose a cost-effective data augmentation and training framework that systematically exploits imperfect LLM-Generated RTL as training data for netlist representation learning, forming an end-to-end pipeline from automated code generation to downstream tasks. We conduct evaluations on circuit functional understanding tasks, including sub-circuit boundary identification and component classification, across benchmarks of increasing scales, extending the task scope from operator-level to IP-level. The evaluations demonstrate that models trained on our noisy synthetic corpus generalize well to real-world netlists, matching or even surpassing methods trained on scarce high-quality data and effectively breaking the data bottleneck in circuit representation learning.
Executive Summary
This article presents a novel approach to learning netlist representations by leveraging Large Language Models (LLMs) to generate Register-Transfer-Level (RTL) code at scale. Although LLM-generated RTL is often functionally imperfect, the synthesized netlists retain structural patterns indicative of intended functionality. The authors propose a cost-effective data augmentation and training framework that exploits this insight, forming an end-to-end pipeline from automated code generation to downstream tasks. Evaluations on circuit functional understanding tasks demonstrate that models trained on noisy synthetic data generalize well to real-world netlists, matching or surpassing methods trained on scarce high-quality data. This breakthrough has the potential to significantly alleviate the data bottleneck in circuit representation learning, enabling the analysis of larger and more complex designs.
Key Points
- ▸ LLMs can generate RTL code at scale, but their functional incorrectness has hindered their use in circuit analysis.
- ▸ Imperfect LLM-generated RTL can still preserve structural patterns indicative of intended functionality.
- ▸ A cost-effective data augmentation and training framework is proposed to leverage this insight.
Merits
Strength in scalability
The proposed framework can handle large-scale designs that are difficult to annotate and analyze using traditional methods.
Improved generalizability
Models trained on noisy synthetic data can generalize well to real-world netlists, reducing the dependence on scarce high-quality data.
Demerits
Potential for errors
The use of imperfect LLM-generated RTL may introduce errors or biases in the netlist representations, which need to be carefully evaluated and mitigated.
Limited understanding of structural patterns
The article assumes that structural patterns are strongly indicative of intended functionality, but the extent to which this is true remains unclear.
Expert Commentary
The article presents a significant advancement in circuit representation learning, leveraging the potential of LLMs to generate RTL code at scale. While the proposed framework has its limitations, it demonstrates a promising approach to alleviating the data bottleneck in circuit analysis. The results are encouraging, and further research is needed to fully understand the structural patterns indicative of intended functionality and to evaluate the robustness of the proposed framework. The implications of this breakthrough are substantial, and the authors' work has the potential to transform the field of circuit design and analysis.
Recommendations
- ✓ Further research is needed to evaluate the robustness and generalizability of the proposed framework across different domains and designs.
- ✓ The authors should investigate the extent to which structural patterns are indicative of intended functionality and explore ways to improve the accuracy of these patterns.
