  The deployment of convolutional neural networks is often hindered by high
computational and storage requirements. Structured model pruning is a promising
approach to alleviate these requirements. Using the VGG-16 model as an example,
we measure the accuracy-efficiency trade-off for various structured model
pruning methods and datasets (CIFAR-10 and ImageNet) on Tensor Processing Units
(TPUs). To measure the actual performance of models, we develop a structured
model pruning library for TensorFlow2 to modify models in place (instead of
adding mask layers). We show that structured model pruning can significantly
improve model memory usage and speed on TPUs without losing accuracy,
especially for small datasets (e.g., CIFAR-10).

Machine Learning;Hardware Architecture