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AI in Semiconductors

Princeton University_070820A
(Photo: Princeton University, Office of Communications)

 

- Overview

Artificial intelligence (AI) is transforming the semiconductor industry in many ways: 

  • Design: AI can reduce design time, improve performance, and provide early-stage feedback. AI can also help chip makers get more chips from each design and finish them faster.
  • Production: AI can help reduce material losses, improve production efficiency, and reduce production times.
  • Process: AI can be present at all process points, from design to verification and testing.

 

- AI-driven Chip Design

AI-driven chip design uses machine learning (ML) technologies to design, verify, and test semiconductor devices. 

AI can:

  • Explore design approaches: AI algorithms can explore design approaches faster than humans can, helping to identify areas for power optimization.
  • Automate tasks: AI can automate tasks such as place and route, and debug.
  • Reduce manual aspects: AI can reduce the need for manual aspects of the design process, such as layout design and optimization.
  • Process data: AI chips can handle more variables and computational nuances than conventional processors, and process exponentially more data.
  • Improve performance: AI chips can improve performance by optimizing tasks and providing the capacity for larger datasets.

AI chips include graphics processing units (GPUs), field-programmable gate arrays (FPGAs), and application-specific integrated circuits (ASICs) that are specialized for AI.

 

 

- AI-Driven EDA Tools

Machine learning (ML) can be used in electronic design automation (EDA) tools in several ways: 

  • Replace heuristics: ML can be used to replace existing heuristics in EDA tools, such as implementation and sign-off verification tools.
  • Identify defects: ML can help identify potential defects and inefficiencies in real-time.
  • Predict trends: ML can be used to predict future trends and optimize processes for greater efficiency.
  • Improve EDA methods: ML can be used to improve EDA methods, covering almost all the stages in the chip design flow. These stages include: design space reduction and exploration, logic synthesis, placement, routing, testing, verification.


The choice of AI development tools can determine whether it will be easy or difficult to incorporate machine intelligence into EDA tools. AI researchers have developed a long list of frameworks, libraries, and languages that they use to develop AI and machine-learning software.

 

[More to come ...]


 

 

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