The number of memory choices and architectures is exploding, driven by the rapid evolution in AI and machine learning chips being designed for a wide range of very different end markets and systems.

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Google researchers have revealed that memory and interconnect are the primary bottlenecks for LLM inference, not compute power, as memory bandwidth lags 4.7x behind.

While there are several theories of memory that describe how learners take in, store, and retrieve information, the simplest theory for our purposes breaks memory into the following parts: For the ...