  Neural network pruning is one of the most popular methods of accelerating the
inference of deep convolutional neural networks (CNNs). The dominant pruning
methods, filter-level pruning methods, evaluate their performance through the
reduction ratio of computations and deem that a higher reduction ratio of
computations is equivalent to a higher acceleration ratio in terms of inference
time. However, we argue that they are not equivalent if parallel computing is
considered. Given that filter-level pruning only prunes filters in layers and
computations in a layer usually run in parallel, most computations reduced by
filter-level pruning usually run in parallel with the un-reduced ones. Thus,
the acceleration ratio of filter-level pruning is limited. To get a higher
acceleration ratio, it is better to prune redundant layers because computations
of different layers cannot run in parallel. In this paper, we propose our
Discrimination based Block-level Pruning method (DBP). Specifically, DBP takes
a sequence of consecutive layers (e.g., Conv-BN-ReLu) as a block and removes
redundant blocks according to the discrimination of their output features. As a
result, DBP achieves a considerable acceleration ratio by reducing the depth of
CNNs. Extensive experiments show that DBP has surpassed state-of-the-art
filter-level pruning methods in both accuracy and acceleration ratio. Our code
will be made available soon.
