182 citations · 388 across the 6 of their papers we have counts for
6 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,…
WRPN: Training and Inference using Wide Reduced-Precision Networks
Asit Mishra, Jeffrey J Cook, Eriko Nurvitadhi +1
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…
Accelerating Deep Convolutional Networks using low-precision and sparsity
Ganesh Venkatesh, Eriko Nurvitadhi, Debbie Marr
We explore techniques to significantly improve the compute efficiency and performance of Deep Convolution Networks without impacting their accuracy. To improve the compute efficien…