Memory Systems for AI: Part 2

By Rambus Inc.

February 27, 2020

In part one of this series, we discussed how the world’s digital data is growing exponentially, doubling approximately every two years. In fact, there’s so much digital data in the world that artificial intelligence (AI) is practically the only way to begin to make sense of it all in a timely fashion. Insights gleaned from digital data are becoming more valuable, and one side effect is the need for greater security to protect the data, the AI models, and the infrastructure as well. Not surprisingly, the increasing value of the data and insights is causing AI developers to want to create more sophisticated algorithms, larger datasets, and new use cases and applications.

The challenge? Everyone wants more performance. However, the semiconductor industry can no longer fully rely on two important tools – Moore’s Law and Dennard (power) scaling – that have powered successive generations of silicon for the past several decades. Moore’s Law is slowing, while Dennard Scaling broke down around 2005. Nevertheless, the explosion of data and the advent of new AI applications are challenging the semiconductor industry to find new ways to provide better performance and better power efficiency.

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