This paper proposes PuRL - a deep reinforcement learning (RL) based algorithm for pruning neural
networks. Unlike current RL based model compression approaches where feedback is given only
at the end of each episode to the agent, PuRL provides rewards at every pruning step. This enables
PuRL to achieve sparsity and accuracy comparable to current state-of-the-art methods, while having a much shorter training cycle. PuRL achieves more than 80% sparsity on the ResNet-50 model
while retaining a Top-1 accuracy of 75.37% on the ImageNet dataset. Through our experiments
we show that PuRL is also able to sparsify already efficient architectures like MobileNet-V2. In
addition to performance characterisation experiments, we also provide a discussion and analysis of
the various RL design choices that went into the tuning of the Markov Decision Process underlying PuRL. Lastly, we point out that PuRL is simple to use and can be easily adapted for various
architectures.
License type:
http://creativecommons.org/licenses/by-nc-nd/4.0/
Funding Info:
Agency for Science, Technology and Research (A*STAR) under
its AME Programmatic Funds (Project No.A1892b0026 and No.A19E3b0099)