MULTI-HARDWARE ENERGY-CONSUMPTION-ORIENTED CHANNEL PRUNING METHOD AND RELATED PRODUCT

Abstract

A multi-hardware energy-consumption-oriented channel pruning method and a related product. The method includes: ranking importance of a filter in a to-be-pruned convolutional neural network (CNN) model by using a feature distribution discrepancy (FDD) evaluation model based on a feature distribution of an original network model, and deleting a filter with a lowest importance ranking to generate a candidate first pruning model; determining an energy consumption of the candidate first pruning model by using an energy consumption estimation model based on actual measured data; performing trade-off processing on importance of a filter in the candidate first pruning model and the energy consumption of the candidate first pruning model by using a multi-objective evolutionary solving model, and obtaining a pruning scheme corresponding to each hardware device; and pruning the to-be-pruned CNN model by using the pruning scheme, and obtaining a second pruning model corresponding to each hardware device.

CPC Classifications