This BNN reaches a 77. 9% accuracy, just 7% lower than the full-precision version, with 58 kB (7. 2 times less) for the weights and 262 kB (2. 4 times less) memory in total.
Furthermore, it can perform inference on a binarized ResNet-18 trained with 8-bases Group-Net to achieve a 67. 5% Top-1 accuracy with only 3. 0 mJ/frame -- at an accuracy drop of merely 1. 8% from the full-precision ResNet-18.
Radio Resource Management (RRM) in 5G mobile communication is a challenging problem for which Recurrent Neural Networks (RNN) have shown promising results.
Deploying state-of-the-art CNNs requires power-hungry processors and off-chip memory.
Convolutional neural networks (CNNs) have revolutionized the world of computer vision over the last few years, pushing image classification beyond human accuracy.