1 citations · 1 across the 2 of their papers we have counts for
6 papers
DNN Quantization with Attention
Ghouthi Boukli Hacene, Lukas Mauch, Stefan Uhlich +1
Low-bit quantization of network weights and activations can drastically reduce the memory footprint, complexity, energy consumption and latency of Deep Neural Networks (DNNs). Howe…
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…
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…
Deep Neural Network inference with reduced word length
Lukas Mauch, Bin Yang
Deep neural networks (DNN) are powerful models for many pattern recognition tasks, yet their high computational complexity and memory requirement limit them to applications on high…
A Machine-learning framework for automatic reference-free quality assessment in MRI
Thomas Küstner, Sergios Gatidis, Annika Liebgott +9
Magnetic resonance (MR) imaging offers a wide variety of imaging techniques. A large amount of data is created per examination which needs to be checked for sufficient quality in o…
Neural Network Ensembles to Real-time Identification of Plug-level Appliance Measurements
Karim Said Barsim, Lukas Mauch, Bin Yang
The problem of identifying end-use electrical appliances from their individual consumption profiles, known as the appliance identification problem, is a primary stage in both Non-I…