From https://www.linkedin.com/pulse/ai-strategy-netlist-eco-heidi-zheng-6nyhc
NanDigits, a leading provider of Verilog Netlist ECO and debug tools, is committed to leveraging artificial intelligence (AI) to enhance its technical support, improve the ECO engine, and elevate the user experience. Here's an overview of NanDigits' AI strategy, focusing on its current use, future plans, and measurement of success.
NanDigits currently employs AI in its technical support to streamline workflows and improve user experience:
Automatic ECO Script Generation: AI generates scripts based on users' natural language descriptions, minimizing manual effort and accelerating the ECO process.
Enhanced Search Performance: The platform leverages advanced Retrieval-Augmented Generation (RAG) technology through two key approaches. The first involves combining semantic and keyword searches, while the second integrates multiple embedding models, specifically Mistral Embedding and SBERT. Together, these methods boost search accuracy by over 20%.
Multilingual Support: The system seamlessly handles English, Chinese, Korean, and Japanese, automatically switching between these languages according to user input.
Continuous Conversations: The platform supports ongoing dialogues with built-in chat history, ensuring a smooth and cohesive user experience.
NanDigits plans to further enhance its AI capabilities in technical support by:
Integrating AI into the ECO Engine:
Adaptive Learning: The AI system will learn from past ECO operations to adapt and improve the ECO engine's performance over time.
Predictive Analysis: AI will predict the impact of ECO changes on the netlist, helping to identify potential issues before they occur.
Optimization Algorithms: AI-driven optimization algorithms will find the most efficient ECO patches, minimizing gate count and reducing the size of the ECO patch.
Parallel Processing Optimization: AI will help optimize the use of multiple CPU cores for parallel processing, further reducing ECO run times.
Ensuring Accuracy and Reliability of AI-driven Predictions and Optimizations: NanDigits will focus on data quality, model validation, continuous monitoring, explainable AI, and regular model updates to ensure the accuracy and reliability of AI-driven predictions and optimizations.
NanDigits will measure and evaluate the success of its AI integration into the ECO engine using various metrics and methods, such as:
Performance metrics (ECO patch size reduction, ECO run time reduction, accuracy, and reliability).
User satisfaction and feedback (user surveys, interviews, and Net Promoter Score).
AI model performance (model accuracy, loss, retraining frequency, and updates).
AI-driven ECO adoption rate (user adoption, AI-driven ECO vs. manual ECO).
Cost savings and ROI (resource efficiency, return on investment).
By focusing on these areas, NanDigits aims to create a more efficient, user-friendly, and secure ECO engine, ultimately enhancing the user experience and driving business growth through AI-driven innovation.
