39 citations · 45 across the 4 of their papers we have counts for
10 papers
FAT: Training Neural Networks for Reliable Inference Under Hardware Faults
Ussama Zahid, Giulio Gambardella, Nicholas J. Fraser +2
Deep neural networks (DNNs) are state-of-the-art algorithms for multiple applications, spanning from image classification to speech recognition. While providing excellent accuracy,…
Efficient Error-Tolerant Quantized Neural Network Accelerators
Giulio Gambardella, Johannes Kappauf, Michaela Blott +4
Neural Networks are currently one of the most widely deployed machine learning algorithms. In particular, Convolutional Neural Networks (CNNs), are gaining popularity and are evalu…
Synetgy: Algorithm-hardware Co-design for ConvNet Accelerators on Embedded FPGAs
Yifan Yang, Qijing Huang, Bichen Wu +8
Using FPGAs to accelerate ConvNets has attracted significant attention in recent years. However, FPGA accelerator design has not leveraged the latest progress of ConvNets. As a res…
FINN-R: An End-to-End Deep-Learning Framework for Fast Exploration of Quantized Neural Networks
Michaela Blott, Thomas Preusser, Nicholas Fraser +3
Convolutional Neural Networks have rapidly become the most successful machine learning algorithm, enabling ubiquitous machine vision and intelligent decisions on even embedded comp…
Accuracy to Throughput Trade-offs for Reduced Precision Neural Networks on Reconfigurable Logic
Jiang Su, Nicholas J. Fraser, Giulio Gambardella +5
Modern CNN are typically based on floating point linear algebra based implementations. Recently, reduced precision NN have been gaining popularity as they require significantly les…
FINN-L: Library Extensions and Design Trade-off Analysis for Variable Precision LSTM Networks on FPGAs
Vladimir Rybalkin, Alessandro Pappalardo, Muhammad Mohsin Ghaffar +3
It is well known that many types of artificial neural networks, including recurrent networks, can achieve a high classification accuracy even with low-precision weights and activat…