Effective Network Compression Using Simulation-Guided Iterative Pruning

Dae-Woong Jeong, Jaehun Kim, Youngseok Kim, Tae-Ho Kim, Myungsu Chae

Existing high-performance deep learning models require very intensive computing. For this reason, it is difficult to embed a deep learning model into a system with limited resources. In this paper, we propose the novel idea of the network compression as a method to solve this limitation. The principle of this idea is to make iterative pruning more effective and sophisticated by simulating the reduced network. A simple experiment was conducted to evaluate the method; the results showed that the proposed method achieved higher performance than existing methods at the same pruning level.

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