182 citations · 388 across the 6 of their papers we have counts for
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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…