Hardware for machine learning

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Training repeats the same calculation over millions of examples for hours or days, needing massive parallel processing. Using the trained model is one small calculation per question, which a phone or camera can often do.

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Both provide the massive parallel processing training needs. A GPU has thousands of general simple cores; a TPU is a chip designed specifically for machine-learning calculations.

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An ASIC is built for one task and can never do another — the fastest and most power-efficient option, but useless if the task changes. An FPGA can be reconfigured after manufacture, so it is slower but adaptable.

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When a trained model must answer immediately on the spot — in a camera or a vehicle. It gives millisecond responses, keeps data off the network, and works even when the connection drops.

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Training datasets run to terabytes and must be read fast enough to keep the processors busy. Slow storage leaves very expensive hardware sitting idle and waiting.