98 citations · 102 across the 5 of their papers we have counts for
7 papers
Differentiable Transportation Pruning
Yunqiang Li, Jan C. van Gemert, Torsten Hoefler +3
Deep learning algorithms are increasingly employed at the edge. However, edge devices are resource constrained and thus require efficient deployment of deep neural networks. Prunin…
Distilling Optimal Neural Networks: Rapid Search in Diverse Spaces
Bert Moons, Parham Noorzad, Andrii Skliar +4
Current state-of-the-art Neural Architecture Search (NAS) methods neither efficiently scale to multiple hardware platforms, nor handle diverse architectural search-spaces. To remed…
BinarEye: An Always-On Energy-Accuracy-Scalable Binary CNN Processor With All Memory On Chip in 28nm CMOS
Bert Moons, Daniel Bankman, Lita Yang +2
This paper introduces BinarEye: a digital processor for always-on Binary Convolutional Neural Networks. The chip maximizes data reuse through a Neuron Array exploiting local weight…
Resource aware design of a deep convolutional-recurrent neural network for speech recognition through audio-visual sensor fusion
Matthijs Van keirsbilck, Bert Moons, Marian Verhelst
Today's Automatic Speech Recognition systems only rely on acoustic signals and often don't perform well under noisy conditions. Performing multi-modal speech recognition - processi…
Minimum Energy Quantized Neural Networks
Bert Moons, Koen Goetschalckx, Nick Van Berckelaer +1
This work targets the automated minimum-energy optimization of Quantized Neural Networks (QNNs) - networks using low precision weights and activations. These networks are trained f…
A 0.3-2.6 TOPS/W Precision-Scalable Processor for Real-Time Large-Scale ConvNets
Bert Moons, Marian Verhelst
A low-power precision-scalable processor for ConvNets or convolutional neural networks (CNN) is implemented in a 40nm technology. Its 256 parallel processing units achieve a peak 1…