SynthAI

Inspiration

The growing demand for fine-tuned large language models (LLMs) across industries inspired us to develop a seamless AI-powered solution for hardware designers. SynthAI enables businesses to generate high-quality Verilog RTL (Register Transfer Level) code directly within their data ecosystem, eliminating reliance on external infrastructure. By integrating advanced AI and graph-based methodologies, this project bridges the gap between hardware design and automation.

What it does

SynthAI automates Verilog RTL code generation for hardware design, leveraging DeepSeek, graph-based algorithms with NetworkX, and structured storage using ArangoDB. By transforming textual design descriptions into functional Verilog code, SynthAI significantly enhances efficiency in circuit design workflows.

How we built it

-Data Preparation: Curated and structured a dataset mapping design descriptions to Verilog implementations, enabling high-quality model training. -Graph-Based Representation: Utilized NetworkX to model RTL components and their interconnections, optimizing the logical structure of generated Verilog code. -Model Fine-Tuning: Applied DeepSeek to refine the code generation process, ensuring both syntactic correctness and functional integrity. -Data Management & Retrieval: Employed ArangoDB for efficient storage and retrieval, facilitating seamless interaction between the AI model and stored design knowledge.

Challenges we faced

-Limited Public Datasets: High-quality datasets for RTL code generation are scarce, requiring extensive curation and augmentation. -Code Accuracy & Reliability: Ensuring that generated Verilog code meets both syntactic and functional correctness standards was a key challenge. -Graph Optimization: Structuring RTL logic efficiently using NetworkX required advanced optimization techniques to improve design quality.

What’s next for SynthAI

-SynthAI aims to redefine Verilog module design by evolving into a full-scale AI-driven hardware automation tool. Our future roadmap includes: -EDA Tool Integration: Streamlining verification, synthesis, and simulation workflows directly from the generated Verilog code. -Industry-Specific Fine-Tuning: Enhancing the model with real-world datasets from industry partners to align with evolving design standards. -Advanced Graph Optimization: Further refining algorithms to improve efficiency and accuracy in RTL structure generation.

Built With

• azure-cloud
• azure-cosmos-db
• azure-openai-service
• networkx
• python