Timezone: »

 
Poster
Moonshine: Distilling with Cheap Convolutions
Elliot Crowley · Gavia Gray · Amos Storkey

Tue Dec 04 07:45 AM -- 09:45 AM (PST) @ Room 210 #46
in Tue Poster Session A »

Many engineers wish to deploy modern neural networks in memory-limited settings; but the development of flexible methods for reducing memory use is in its infancy, and there is little knowledge of the resulting cost-benefit. We propose structural model distillation for memory reduction using a strategy that produces a student architecture that is a simple transformation of the teacher architecture: no redesign is needed, and the same hyperparameters can be used. Using attention transfer, we provide Pareto curves/tables for distillation of residual networks with four benchmark datasets, indicating the memory versus accuracy payoff. We show that substantial memory savings are possible with very little loss of accuracy, and confirm that distillation provides student network performance that is better than training that student architecture directly on data.

Keywords: Hardware and Systems · CNN Architectures
[ Paper] 

Author Information

Elliot Crowley (University of Edinburgh)
Gavia Gray (University of Edinburgh)
Amos Storkey (University of Edinburgh)

More from the Same Authors