ChipNeMo: Domain-Adapted LLMs for Chip Design (AI summary)

Key Points

1) The study explores the applications of large language models (LLMs) for industrial chip design and adopts domain adaptation techniques to improve LLM performance for chip design applications, such as engineering assistant chatbot, EDA script generation, and bug summarization and analysis.

2) Domain adaptation techniques including custom tokenizers, domain-adaptive continued pretraining, supervised fine-tuning with domain-specific instructions, and domain-adapted retrieval models enable significant LLM performance improvements over general-purpose base models, allowing up to 5x model size reduction with similar or better performance on design tasks.

3) ChipNeMo foundation models are completion models that require supervised fine-tuning to adapt to tasks such as chat. Fine-tuning with domain-specific instructions significantly improves application proficiency.

4) The paper provides detailed experimental results, including human evaluations, auto evaluation benchmarks for domain knowledge verification, and domain-adapted retrieval model accuracy comparisons.

5) The study also presents ablation studies, exploring the impact of tokenizers, domain adaptation, and training cost on LLM performance.

6) The research findings indicate that domain-adapted LLM effectiveness is observed on multiple-choice domain-specific AutoEval benchmarks and human evaluations for applications.

7) The domain-adapted models dramatically outperform all vanilla LLMs on both multiple-choice domain-specific AutoEval benchmarks and human evaluations for applications, achieving significant improvements in internal design knowledge and general circuit design knowledge.

8) Domain-adapted retrieval models improve the retriever hit rate by 30% over a pre-trained state-of-the-art retriever, demonstrating significant improvements in model quality.

9) The paper concludes by discussing the potential implications of the research findings for agent-based design methodologies and areas for further investigation and improvement in domain-adapted LLM approaches for chip design.

Summary

Domain-Adapted Large Language Models in Chip Design
The paper explores the applications of large language models (LLMs) in industrial chip design and demonstrates the effectiveness of domain-adapted LLMs in three specific use cases: an engineering assistant chatbot, EDA (Electronic Design Automation) script generation, and bug summarization and analysis. The study proposes various domain adaptation techniques including custom tokenizers, domain-adaptive continued pretraining, and supervised fine-tuning with domain-specific instructions. The domain-adapted models achieved substantial improvements in LLM performance over general-purpose base models.

The research findings point out that domain-adapted LLM approaches can significantly enhance performance on domain-specific tasks and close the gap between the domain-adapted models and ideal outcomes. The paper also discusses the construction and augmentation of chip design datasets, incorporating public datasets, and conducting downsampling and upsampling of domain data to enhance the model's comprehension of domain-specific knowledge. Additionally, the study presents the hyperparameters and results of domain-adaptive pretraining, tokenizer augmentation, and suggestions for training cost optimization. The proposed approach and findings have significant implications for improving productivity in the chip design process using AI technology.

The research paper explores the application of AI technology in Electronic Design Automation (EDA) algorithms and the chip design process to enhance productivity. It focuses on the use of large language models (LLMs) to automate language-related chip design tasks such as code generation, response generation, bug triage, and bug summarization. The paper recommends domain-adaptive pre-training, model alignment, and retrieval-augmented generation (RAG) techniques for cost-effective training of domain-specific models and demonstrates the effectiveness of domain-adapted LLMs in engineering assistant chatbot, EDA script generation, and bug summarization and analysis tasks. The study also discusses the construction and augmentation of chip design datasets, incorporating public datasets, downsampling, and upsampling of domain data to improve the model's comprehension of domain-specific knowledge.

Furthermore, the paper introduces Parameter Efficient Fine-Tuning (PEFT) and Low-Rank Adaptation (LoRA) techniques for efficient fine-tuning of downstream tasks in DAPT. It presents evaluations of the domain-adapted models, discussing the impact of public dataset mix-in and learning rate on model performance.

Automatic Training Sample Generation
Additionally, the paper describes a process to automatically generate training samples using contrastive learning and retrieval models. It compares hits from retrieval models and presents a sample generation procedure. The paper also includes detailed evaluation results for the engineering assistant chatbot, EDA script generation, and bug summarization and analysis tasks.

AI-Based Solutions for Chip Design Issues
Moreover, the research paper discusses AI-based solutions to specific queries related to System Verilog assert, retiming stages in chip design, and running multiple seeds of random tests using the Testgen framework. It also provides code snippets for EDA script generation and bug summarization and analysis tasks. The bug summary and analysis section includes technical and managerial summaries, as well as a task assignment related to evaluating silicon current for fabric macros in a chip.

Reference: https://arxiv.org/abs/2311.00176