![]() So Google’s researchers Goldie and Mirhoseini modelled chip placement as a reinforcement learning problem. Even with today’s advanced tools, it takes a human expert week of iteration to produce an acceptable design. For those of us that have worked with PCB, we can understand the struggle. 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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