49 citations · 66 across the 4 of their papers we have counts for
4 papers
Efficient Sampling for Predictor-Based Neural Architecture Search
Lukas Mauch, Stephen Tiedemann, Javier Alonso Garcia +4
Recently, predictor-based algorithms emerged as a promising approach for neural architecture search (NAS). For NAS, we typically have to calculate the validation accuracy of a larg…
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…
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…
Iteratively Training Look-Up Tables for Network Quantization
Fabien Cardinaux, Stefan Uhlich, Kazuki Yoshiyama +4
Operating deep neural networks on devices with limited resources requires the reduction of their memory footprints and computational requirements. In this paper we introduce a trai…