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Found 2 papers, 1 papers with code
Reducing Activation Recomputation in Large Transformer Models
In this paper, we show how to significantly accelerate training of large transformer models by reducing activation recomputation.
Sparse Persistent RNNs: Squeezing Large Recurrent Networks On-Chip
Recurrent Neural Networks (RNNs) are powerful tools for solving sequence-based problems, but their efficacy and execution time are dependent on the size of the network.
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