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We call our new hardware a sampling unit, not a processing unit, because TSUs perform an entirely different type of operation than CPUs and GPUs. Instead of processing a series of programmable deterministic computations, TSUs produce samples from a programmable distribution. Running a generative AI algorithm fundamentally comes down to sampling from some complicated probability distribution. Modern AI systems do a lot of matrix multiplication to produce a vector of probabilities, and then sample from that. Our hardware skips the matrix multiplication and directly samples from complex probability distributions. Specifically, TSUs sample from energy-based models, which are a type of machine learning model that directly define the shape of a probability distribution via an energy function. The inputs to a TSU are parameters that specify the energy function of an EBM, and the outputs of a TSU are samples from the defined EBM. To use a TSU for machine learning, we fit the parameters of the energy function so that the EBM running on the TSU is a good model of some real-world data.
