Cloud EDA AI Expert Module for Multiuser Knowledge Sharing
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Solution Overview
Problem
Existing electronic design automation (EDA) tools face challenges in knowledge sharing among multiple users, despite incorporating features like automatic simulation and AI/ML algorithms.
Innovation Solution
A cloud-based EDA system with an AI expert module that learns and evolves using deep learning algorithms, processes user data to refine design methodologies, and updates an AI agent on user devices for enhanced design assistance, utilizing a neural network and reinforcement learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If EDA tools incorporate AI/ML algorithms and automatic simulation features, then design automation capability is improved, but knowledge sharing among multiple users deteriorates
Solution Approach 1:
The patent merges individual user knowledge with cloud-based AI expertise by integrating local EDA tools with cloud AI services. The hybrid architecture combines local automation capabilities with centralized knowledge repositories, enabling seamless knowledge sharing across multiple users while maintaining design automation functionality.
Solution Approach 2:
The cloud-based AI expert system acts as an intermediary between multiple users and the knowledge base. It receives queries from local EDA tools, processes them against the centralized knowledge repository, and returns refined design methodologies, thereby facilitating knowledge sharing without compromising local automation capabilities.
2Manufacturing precision
If cloud-based AI expert module processes data from multiple users, then design methodology refinement is improved, but system complexity increases
Solution Approach 1:
The system is segmented into distinct functional modules: local EDA tools on user devices, cloud-based AI expert module, and centralized knowledge base. This segmentation allows each component to specialize in specific tasks, improving design methodology refinement while managing system complexity through modular architecture.
Solution Approach 2:
The cloud-based AI expert module serves multiple functions: processing queries from various users, refining design methodologies, updating the knowledge base, and providing recommendations. This multi-functionality consolidates complex operations into a single universal component, reducing overall system complexity.
3Ease of operation
If AI agent on user device receives continuous updates from cloud, then user assistance quality is improved, but communication overhead increases
Solution Approach 1:
The system implements periodic updates where the AI agent on the user device receives refined design methodologies and knowledge updates from the cloud at scheduled intervals or triggered by specific events. This periodic communication reduces overhead compared to continuous data exchange while maintaining high user assistance quality.
Solution Approach 2:
The AI agent on the user device is designed to autonomously manage updates by selectively requesting only the knowledge refinements relevant to current design tasks. This self-service approach minimizes unnecessary communication overhead while ensuring the agent receives timely updates for improved user assistance.
Data Source
AI summary
A cloud-based computer system for electronic design automation (EDA) is provided. The cloud-based computer system may include one or more processors, and a memory storing instructions executable by the one or more processors. The instructions, when executed, may cause the system to provide a cloud EDA artificial intelligence (AI) expert module to learn and evolve in an electronic designing field using a new set of electronic design methodologies data, store a new set of refined electronic design methodologies data, and update an AI agent associated with an EDA tool executed on a user device. The AI agent may receive a set of EDA-related knowledge data associated with a user activity on the EDA tool, transmit the received data to a cloud EDA AI expert module for processing, and receive the new set of refined electronic design methodologies data to enhance user assistance in the electronic design process.