A new Silicon Integration Initiative white paper identifies a common data model as the most critical need to accelerate the use of artificial intelligence and machine learning in semiconductor electronic design automation.
The white paper, produced by a 20-member Si2 Special Interest Group, reports on findings of a global survey that identifies planned usage and structural gaps for AI and ML in EDA.
Leigh Anne Clevenger, Si2 senior data scientist, said that the white paper identifies “a standard, common model for classifying and structuring machine learning and inference data as being crucial to accelerating the use of AI/ML in EDA. This data model would provide a foundation for addressing the data organization gap for chip developers, EDA tool developers, IP providers, and researchers. It would support design data and derived data for high-interest use cases.”
The survey also identifies a common reference flow, on-line AI/ML courses and organized training data as industry needs.
The white paper addresses:
Machine Learning and IC Design
Demand for Data
Structure of a Data Model
A Unified Data Model: Digital and Analog Examples
Definition and Characteristics of Derived Data for ML Applications
Need for IP Protection
Unique Requirements for Inferencing Models
Key Analysis Domains
Conclusions and Proposed Future Work
Members of the Si2 Special Interest Group include:
Advanced Micro Devices
Cadence Design Systems
Hewlett Packard Enterprise
Mentor, a Siemens Business
NC State University
Sandia National Laboratories
The white paper is available at https://si2.org/product/collaborative-data-model/
Founded in 1988, Si2 is a leading research and development joint venture that provides standard interoperability solutions for integrated circuit design tools. All Si2 activities are carried out under the auspices of The National Cooperative Research and Production Act of 1993, the fundamental law that defines R&D joint ventures and offers them a large measure of protection against federal antitrust laws.
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