Artificial intelligence presents a major challenge to conventional computing architecture. In standard models, memory storage and computing take place in different parts of the machine, and data must move from its area of storage to a CPU or GPU for processing.
The problem with this design is that movement takes time. Too much time. You can have the most powerful processing unit on the market, but its performance will be limited as it idles waiting for data, a problem known as the “memory wall” or “bottleneck.”
When computing outperforms memory transfer, latency is unavoidable. These delays become serious problems when dealing with the enormous amounts of data essential for machine learning and AI applications.
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