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Differentiable Branching in Deep Networks for Fast Inference

Citation Author(s):
Simone Scardapane, Danilo Comminiello, Michele Scarpiniti, Enzo Baccarelli, Aurelio Uncini
Submitted by:
Danilo Comminiello
Last updated:
5 June 2020 - 4:28am
Document Type:
Presentation Slides
Document Year:
Danilo Comminiello
Paper Code:


In this paper, we consider the design of deep neural networks augmented with multiple auxiliary classifiers departing from the main (backbone) network. These classifiers can be used to perform early-exit from the network at various layers, making them convenient for energy-constrained applications such as IoT, embedded devices, or Fog computing. However, designing an optimized early-exit strategy is a difficult task, generally requiring a large amount of manual fine-tuning. In this paper, we propose a way to jointly optimize this strategy together with the branches, providing an end-to-end trainable algorithm for this emerging class of neural networks. We achieve this by replacing the original output of the branches with a ‘soft’, differentiable approximation. In addition, we also propose a regularization approach to trade-off the computational efficiency of the early-exit strategy with respect to the overall classification accuracy. We evaluate our proposed design approach on a set of image classification benchmarks, showing significant gains in accuracy and inference time.

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Paper #1402_Presentation.pdf