182 citations · 358 across the 3 of their papers we have counts for
4 papers
WRPN & Apprentice: Methods for Training and Inference using Low-Precision Numerics
Asit Mishra, Debbie Marr
Today's high performance deep learning architectures involve large models with numerous parameters. Low precision numerics has emerged as a popular technique to reduce both the com…
Apprentice: Using Knowledge Distillation Techniques To Improve Low-Precision Network Accuracy
Asit Mishra, Debbie Marr
Deep learning networks have achieved state-of-the-art accuracies on computer vision workloads like image classification and object detection. The performant systems, however, typic…
Low Precision RNNs: Quantizing RNNs Without Losing Accuracy
Supriya Kapur, Asit Mishra, Debbie Marr
Similar to convolution neural networks, recurrent neural networks (RNNs) typically suffer from over-parameterization. Quantizing bit-widths of weights and activations results in ru…
WRPN: Wide Reduced-Precision Networks
Asit Mishra, Eriko Nurvitadhi, Jeffrey J Cook +1
For computer vision applications, prior works have shown the efficacy of reducing numeric precision of model parameters (network weights) in deep neural networks. Activation maps,…