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Found 6 papers, 0 papers with code
WRPN & Apprentice: Methods for Training and Inference using Low-Precision Numerics
Today's high performance deep learning architectures involve large models with numerous parameters.
Apprentice: Using Knowledge Distillation Techniques To Improve Low-Precision Network Accuracy
Low-precision numerics and model compression using knowledge distillation are popular techniques to lower both the compute requirements and memory footprint of these deployed models.
Low Precision RNNs: Quantizing RNNs Without Losing Accuracy
Similar to convolution neural networks, recurrent neural networks (RNNs) typically suffer from over-parameterization.
WRPN: Wide Reduced-Precision Networks
We reduce the precision of activation maps (along with model parameters) and increase the number of filter maps in a layer, and find that this scheme matches or surpasses the accuracy of the baseline full-precision network.
WRPN: Training and Inference using Wide Reduced-Precision Networks
For computer vision applications, prior works have shown the efficacy of reducing the numeric precision of model parameters (network weights) in deep neural networks but also that reducing the precision of activations hurts model accuracy much more than reducing the precision of model parameters.
Accelerating Deep Convolutional Networks using low-precision and sparsity
To improve the compute efficiency, we focus on achieving high accuracy with extremely low-precision (2-bit) weight networks, and to accelerate the execution time, we aggressively skip operations on zero-values.
