77 citations · 84 across the 4 of their papers we have counts for
4 papers
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…
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…
Deep Neural Network Approximation for Custom Hardware: Where We've Been, Where We're Going
Erwei Wang, James J. Davis, Ruizhe Zhao +5
Deep neural networks have proven to be particularly effective in visual and audio recognition tasks. Existing models tend to be computationally expensive and memory intensive, howe…
Towards Efficient Convolutional Neural Network for Domain-Specific Applications on FPGA
Ruizhe Zhao, Ho-Cheung Ng, Wayne Luk +1
FPGA becomes a popular technology for implementing Convolutional Neural Network (CNN) in recent years. Most CNN applications on FPGA are domain-specific, e.g., detecting objects fr…