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Layer-compensated Pruning for Resource-constrained Convolutional Neural Networks
[article]
2018
arXiv
pre-print
Resource-efficient convolution neural networks enable not only the intelligence on edge devices but also opportunities in system-level optimization such as scheduling. In this work, we aim to improve the performance of resource-constrained filter pruning by merging two sub-problems commonly considered, i.e., (i) how many filters to prune for each layer and (ii) which filters to prune given a per-layer pruning budget, into a global filter ranking problem. Our framework entails a novel algorithm,
arXiv:1810.00518v2
fatcat:yk7l2fflxbejfoshmxqumzmzfm