
FAT: Training Neural Networks for Reliable Inference Under Hardware Faults
Deep neural networks (DNNs) are stateoftheart algorithms for multiple...
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Efficient ErrorTolerant Quantized Neural Network Accelerators
Neural Networks are currently one of the most widely deployed machine le...
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Synetgy: Algorithmhardware Codesign for ConvNet Accelerators on Embedded FPGAs
Using FPGAs to accelerate ConvNets has attracted significant attention i...
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FINNR: An EndtoEnd DeepLearning Framework for Fast Exploration of Quantized Neural Networks
Convolutional Neural Networks have rapidly become the most successful ma...
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FINNL: Library Extensions and Design Tradeoff Analysis for Variable Precision LSTM Networks on FPGAs
It is well known that many types of artificial neural networks, includin...
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Inference of Quantized Neural Networks on Heterogeneous AllProgrammable Devices
Neural networks have established as a generic and powerful means to appr...
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Compressing Low Precision Deep Neural Networks Using SparsityInduced Regularization in Ternary Networks
A low precision deep neural network training technique for producing spa...
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Scaling Binarized Neural Networks on Reconfigurable Logic
Binarized neural networks (BNNs) are gaining interest in the deep learni...
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FINN: A Framework for Fast, Scalable Binarized Neural Network Inference
Research has shown that convolutional neural networks contain significan...
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Giulio Gambardella
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