134 citations · 160 across the 16 of their papers we have counts for
6 papers · 1 filter
On Causal Inference for Data-free Structured Pruning
Martin Ferianc, Anush Sankaran, Olivier Mastropietro +2
Neural networks (NNs) are making a large impact both on research and industry. Nevertheless, as NNs' accuracy increases, it is followed by an expansion in their size, required numb…
Algorithm and Hardware Co-design for Reconfigurable CNN Accelerator
Hongxiang Fan, Martin Ferianc, Zhiqiang Que +4
Recent advances in algorithm-hardware co-design for deep neural networks (DNNs) have demonstrated their potential in automatically designing neural architectures and hardware desig…
Optimizing Bayesian Recurrent Neural Networks on an FPGA-based Accelerator
Martin Ferianc, Zhiqiang Que, Hongxiang Fan +2
Neural networks have demonstrated their outstanding performance in a wide range of tasks. Specifically recurrent architectures based on long-short term memory (LSTM) cells have man…
High-Performance FPGA-based Accelerator for Bayesian Neural Networks
Hongxiang Fan, Martin Ferianc, Miguel Rodrigues +3
Neural networks (NNs) have demonstrated their potential in a wide range of applications such as image recognition, decision making or recommendation systems. However, standard NNs…
ComBiNet: Compact Convolutional Bayesian Neural Network for Image Segmentation
Martin Ferianc, Divyansh Manocha, Hongxiang Fan +1
Fully convolutional U-shaped neural networks have largely been the dominant approach for pixel-wise image segmentation. In this work, we tackle two defects that hinder their deploy…
On the Effects of Quantisation on Model Uncertainty in Bayesian Neural Networks
Martin Ferianc, Partha Maji, Matthew Mattina +1
Bayesian neural networks (BNNs) are making significant progress in many research areas where decision-making needs to be accompanied by uncertainty estimation. Being able to quanti…