  Modern deep neural network (DNN) systems are highly configurable with large a
number of options that significantly affect their non-functional behavior, for
example inference time and energy consumption. Performance models allow to
understand and predict the effects of such configuration options on system
behavior, but are costly to build because of large configuration spaces.
Performance models from one environment cannot be transferred directly to
another; usually models are rebuilt from scratch for different environments,
for example different hardware. Recently, transfer learning methods have been
applied to reuse knowledge from performance models trained in one environment
in another. In this paper, we perform an empirical study to understand the
effectiveness of different transfer learning strategies for building
performance models of DNN systems. Our results show that transferring
information on the most influential configuration options and their
interactions is an effective way of reducing the cost to build performance
models in new environments.
