  Convolutional neural networks (CNNs) are becoming increasingly deeper, wider,
and non-linear because of the growing demand on prediction accuracy and
analysis quality. The wide and deep CNNs, however, require a large amount of
computing resources and processing time. Many previous works have studied model
pruning to improve inference performance, but little work has been done for
effectively reducing training cost. In this paper, we propose ClickTrain: an
efficient and accurate end-to-end training and pruning framework for CNNs.
Different from the existing pruning-during-training work, ClickTrain provides
higher model accuracy and compression ratio via fine-grained
architecture-preserving pruning. By leveraging pattern-based pruning with our
proposed novel accurate weight importance estimation, dynamic pattern
generation and selection, and compiler-assisted computation optimizations,
ClickTrain generates highly accurate and fast pruned CNN models for direct
deployment without any extra time overhead, compared with the baseline
training. ClickTrain also reduces the end-to-end time cost of the
pruning-after-training method by up to 2.3X with comparable accuracy and
compression ratio. Moreover, compared with the state-of-the-art
pruning-during-training approach, ClickTrain provides significant improvements
both accuracy and compression ratio on the tested CNN models and datasets,
under similar limited training time.
