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This invention speeds up neural networks by creating an on/off map from the output of one part of the network. This map then tells the next part of the network which specific calculations it doesn't need to do, saving processing power. The claims narrow this to an apparatus with dedicated electronic parts that manipulate data structures by setting elements to one of two values, specifically for arithmetic or fetch operations.
Why it matters: Filed as large neural networks were gaining widespread adoption. The continued growth in model size and demand for efficient inference on diverse hardware makes dynamic operation culling even more critical now.
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