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Technical Paper

Reliability verification for artificial intelligence and machine learning processor chips

Diagram of multiple connection paths making up the shape of a human brain.
Artificial intelligence (AI) and machine learning (ML) are seeing growing adoption in a wide range of applications. The integrated circuits (ICs) used in AI/ML applications are characterized by large parallel processing computation units, high power dissipation, and complex circuitry that can deliver maximum performance within a strict power budget. Ensuring the reliable design and verification of these complex ICs is critical, since circuit failures in these chips can have major consequences, especially for ICs used in mission-critical applications.

Reliability verification for AI and machine learning processor chips ensures circuit stability and performance within design and power constraints

The processor chips used in AI/ML applications are characterized by large parallel processing computation units, high power dissipation, and complex circuitry that can deliver maximum performance within a strict power budget. Accurate assessment of IC reliability issues such as electromigration, power and clock gating, dynamic voltage and frequency scaling, and electrical overstress are crucial to delivering the intended power efficiency while maintaining compliance with all design constraints.

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