63 citations · 235 across the 27 of their papers we have counts for
3 papers · 1 filter
Iteratively Training Look-Up Tables for Network Quantization
Fabien Cardinaux, Stefan Uhlich, Kazuki Yoshiyama +5
Operating deep neural networks (DNNs) on devices with limited resources requires the reduction of their memory as well as computational footprint. Popular reduction methods are net…
Closing the Training/Inference Gap for Deep Attractor Networks
Cyril Cadoux, Stefan Uhlich, Marc Ferras +1
This paper improves the deep attractor network (DANet) approach by closing its gap between training and inference. During training, DANet relies on attractors, which are computed f…
Mixed Precision DNNs: All you need is a good parametrization
Stefan Uhlich, Lukas Mauch, Fabien Cardinaux +5
Efficient deep neural network (DNN) inference on mobile or embedded devices typically involves quantization of the network parameters and activations. In particular, mixed precisio…