Draft

Inspiration

The increasing demand for fine-tuned large language models (LLMs) across industries inspired us to create a seamless pipeline for hardware designers. VeriFlow enables businesses to generate high-quality Verilog RTL (Register Transfer Level) code directly within their data platform, eliminating the need for external infrastructure. This project bridges the gap between hardware design and automation by integrating cutting-edge AI and graph-based methodologies.

What it does

VeriFlow automates the generation of high-quality Verilog RTL code for hardware design. It leverages DeepSeek, graph-based algorithms using NetworkX, and structured data storage with ArangoDB to transform design descriptions into functional Verilog code, significantly improving efficiency in circuit design.

How we built it

Data Preparation: Collected and structured a dataset of design descriptions paired with corresponding Verilog implementations, enabling high-quality model training. Graph-Based Representation: Used NetworkX to model RTL components and relationships, optimizing the logical flow of generated Verilog code. Model Fine-Tuning: Leveraged DeepSeek to enhance the code generation process, ensuring syntactic correctness and functional accuracy. Data Management & Retrieval: Utilized ArangoDB for efficient storage and retrieval of design data, enabling seamless interaction between the AI model and stored design knowledge.

Challenges we ran into

Data Scarcity: Publicly available, high-quality datasets for RTL code generation are limited, requiring extensive data curation. Code Accuracy & Quality: Ensuring that the generated Verilog code is not only syntactically correct but also functionally robust was a key challenge. Graph Optimization: Designing an effective algorithm to structure RTL logic using NetworkX posed optimization challenges.

What's next for VeriFlow

VeriFlow aims to transform Verilog module design by evolving into a comprehensive AI-driven solution for hardware automation. Future plans include: Integration with EDA Tools: Streamlining verification, synthesis, and simulation workflows directly from the generated code. Industry-Specific Fine-Tuning: Enhancing the model with real-world datasets from industry partners to align with evolving design standards. Advanced Graph Optimization: Refining algorithms to improve the efficiency and accuracy of generated RTL structures.

Built With

• arangodb
• google-cloud
• networkx
• python
• steamlit