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EvoPrunerPool: An Evolutionary Pruner using Pruner Pool for Compressing Convolutional Neural Networks

EvoPrunerPool formulates filter pruning as a search problem for identifying the right set of pruners from a pool of off-the-shelf filter pruners and applying them in appropriate sequence to incrementally sparsify a given Convolutional Neural Network. Since EvoPrunerPool employs the native representation of a popular machine learning framework and filter pruners from a well-known AutoML toolkit the proposed approach is both extensible and generic. Consequently, a typical practitioner can use EvoPrunerPool without any in-depth understanding of filter pruning in specific and model compression in general.

Funded by: MultiCoreWare Inc.