New Uses For AI In Chips

Artificial intelligence is being deployed across a number of new applications, from improving performance and reducing power in a wide range of end devices to spotting irregularities in data movement for security reasons. While most people are familiar with using machine learning and deep learning to distinguish between cats and dogs, emerging applications show how this capability can be used differently. Data prioritization and partitioning, for example, can be utilized to optimize power and performance of a chip or system with no human intervention. And various flavors of AI can be used throughout the design and manufacturing flows to catch errors or flaws that humans cannot. But all of these new components and functions also make designing chips more complex, even at more mature nodes, as probabilities replace finite answers and the number of variables increases. "As you move AI out to the edge, the edge starts to look like the data center," said Frank Ferro, senior director of product management at Rambus.