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Deploying machine learning models on devices is inefficient and costly with general-purpose GPUs and off-the-shelf accelerators in terms of performance and power.

An automated system that converts ML models into optimized custom hardware accelerator designs for significantly better performance per watt-dollar.

Device makers and companies building ML-enabled products who need custom accelerators for their specific models.