Si2 Publishes White Paper on Expanding Use of AI/ML in Semiconductor Electronic Design

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. It is available at

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

Member of the Si2 Special Interest Group include:

  • Advanced Micro Devices
  • Ansys
  • Cadence Design Systems
  • Hewlett Packard Enterprise
  • IBM
  • Intel Corp.
  • Intento Design
  • Keysight Technologies
  • Mentor, a Siemens Business
  • NC State University
  • PDF Solutions
  • Qualcomm
  • Samsung
  • Sandia National Laboratories
  • Silvaco
  • SK Hynix
  • Synopsys
  • Texas Instruments
  • Thrace Systems