  Much recent research has been dedicated to improving the efficiency of
training and inference for image classification. This effort has commonly
focused on explicitly improving theoretical efficiency, often measured as
ImageNet validation accuracy per FLOP. These theoretical savings have, however,
proven challenging to achieve in practice, particularly on high-performance
training accelerators.
  In this work, we focus on improving the practical efficiency of the
state-of-the-art EfficientNet models on a new class of accelerator, the
Graphcore IPU. We do this by extending this family of models in the following
ways: (i) generalising depthwise convolutions to group convolutions; (ii)
adding proxy-normalized activations to match batch normalization performance
with batch-independent statistics; (iii) reducing compute by lowering the
training resolution and inexpensively fine-tuning at higher resolution. We find
that these three methods improve the practical efficiency for both training and
inference. Code available at
https://github.com/graphcore/graphcore-research/tree/main/Making_EfficientNet_More_Efficient .
