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02/04/2022
EcoFlow: Efficient Convolutional Dataflows for Low-Power Neural Network Accelerators
Dilated and transposed convolutions are widely used in modern convolutio...
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11/11/2020
FAT: Training Neural Networks for Reliable Inference Under Hardware Faults
Deep neural networks (DNNs) are state-of-the-art algorithms for multiple...
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06/02/2020
Vyasa: A High-Performance Vectorizing Compiler for Tensor Convolutions on the Xilinx AI Engine
Xilinx's AI Engine is a recent industry example of energy-efficient vect...
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12/16/2019
Efficient Error-Tolerant Quantized Neural Network Accelerators
Neural Networks are currently one of the most widely deployed machine le...
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11/21/2018
Synetgy: Algorithm-hardware Co-design for ConvNet Accelerators on Embedded FPGAs
Using FPGAs to accelerate ConvNets has attracted significant attention i...
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01/12/2017
Scaling Binarized Neural Networks on Reconfigurable Logic
Binarized neural networks (BNNs) are gaining interest in the deep learni...
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12/01/2016
