Browsing School of Engineering by Subject "Quantised Neural Networks"
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IMEC: A Memory-Efficient Convolution Algorithm For Quantised Neural Network Accelerators
(IEEE, 2022)Quantised convolution neural networks (QCNNs) on FPGAs have shown tremendous potential for deploying deep learning on resource constrained devices closer to the data source or in embedded applications. An essential building ...