CGPA: Coarse-Grained Pruning of Activations for Energy-Efficient RNN Inference

Recurrent neural networks (RNNs) perform element-wise multiplications across the activations of gates. We show that a significant percentage of activations are saturated and propose coarse-grained pruning of activations (CGPA) to avoid the computation of entire neurons, based on the activation value...

Ausführliche Beschreibung