![]() ![]() Designers need to place blocks of logic and memory, including clusters of those blocks, in a way that power and performance are maximised while reducing the area of the chip. That’s why Google started developing AI to design AI chips. Ideally, you want a chip that’s optimised to do today’s AI, not the AI of two to five years ago. The challenge is that it takes years to design a chip, while machine learning algorithms move a lot faster than that. They help perform AI algorithms faster and more efficiently. Today, one of the ways of achieving this is with custom-designed machine learning inference chips. AI acceleration began aiming to offload CPU workload in mathematic intense operations.
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