Today Artificial intelligence (AI) is out of the research laboratory and in the realm of practical engineering applications. AI engineering today is largely about running machine learning (ML) models on digital computers, and these models are typically simulations of brain-inspired models such as neural networks, with deep learning (DL) being the most successful example today. With the plateauing out of CPU performance improvements and the end of Moore’s law, even with multi-core CPU machines, the community has turned to hardware accelerators to run their AI models.

Artificial intelligence (AI) is out of the research laboratory and is in the realm of practical engineering applications. AI engineering today is largely about running machine learning (ML) models on digital computers, and these models are typically simulations of brain-inspired models such as neural networks, with deep learning (DL) being the most successful example today. With the plateauing out of CPU performance improvements and the end of Moore’s law, even with multi-core CPU machines, the community has turned to hardware accelerators to run their AI models.

Artificial intelligence (AI) is out of the research laboratory and is in the realm of practical engineering applications. AI engineering today is largely about running machine learning (ML) models on digital computers, and these models are typically simulations of brain-inspired models such as neural networks, with deep learning (DL) being the most successful example today. With the plateauing out of CPU performance improvements and the end of Moore’s law, even with multi-core CPU machines, the community has turned to hardware accelerators to run their AI models.