WRPN: Wide Reduced-Precision Networks

no code implementations • ICLR 2018 • Asit Mishra, Eriko Nurvitadhi, Jeffrey J Cook, Debbie Marr

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

no code implementations • 10 Apr 2017 • Asit Mishra, Jeffrey J Cook, Eriko Nurvitadhi, Debbie Marr

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.

Quantization