14 citations · 14 across the 1 of their papers we have counts for
4 papers
Feed-Forward On-Edge Fine-tuning Using Static Synthetic Gradient Modules
Robby Neven, Marian Verhelst, Tinne Tuytelaars +1
Training deep learning models on embedded devices is typically avoided since this requires more memory, computation and power over inference. In this work, we focus on lowering the…
ZigZag: A Memory-Centric Rapid DNN Accelerator Design Space Exploration Framework
Linyan Mei, Pouya Houshmand, Vikram Jain +2
Building efficient embedded deep learning systems requires a tight co-design between DNN algorithms, memory hierarchy, and dataflow. However, owing to the large degrees of freedom…
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…