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A Collaborative Data Model For AI/ML In EDA

To accelerate AI/ML applications for EDA, a collaborative and coordinated approach is needed.

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This work explores industry perspectives on:

  1. Machine Learning and IC Design
  2. Demand for Data
  3. Structure of a Data Model
  4. A Unified Data Model: Digital and Analog examples
  5. Definition and Characteristics of Derived Data for ML Applications
  6. Need for IP Protection
  7. Unique Requirements for Inferencing Models
  8. Key Analysis Domains
  9. Conclusions and Proposed Future Work

Abstract
A standard, common method for classification and structure of machine learning training and inference data for interoperability is critical to enable and accelerate the use of artificial intelligence and machine learning in semiconductor electronic design automation. Subject matter experts from across the semiconductor and EDA industry highlight the differences and common threads in developing industry standards for AI/ML in EDA application data for design areas including digital, analog, shapes-based and IP development. The authors conclude that in order to accelerate AI/ML applications for EDA, a collaborative and coordinated approach is needed. A prerequisite for this approach is establishing the best process for organizing, leveraging and sharing data. Si2 industry survey results show a gap in the availability and organization for AI/ML data in EDA. A common data model would address the data organization gap for chip developers, EDA tool developers, IP providers and researchers by first supporting the high interest EDA areas, design data and derived data.

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