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EnglishIn 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.
Differentiable branching in deep networks for fast inference / Scardapane, S.; Comminiello, D.; Scarpiniti, M.; Baccarelli, E.; Uncini, A.. - (2020), pp. 4167-4171. ((Intervento presentato al convegno 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 tenutosi a Barcelona, Spain [10.1109/ICASSP40776.2020.9054209].
| Titolo: | Differentiable branching in deep networks for fast inference | |
| Autori: | ||
| Data di pubblicazione: | 2020 | |
| Serie: | ||
| Citazione: | Differentiable branching in deep networks for fast inference / Scardapane, S.; Comminiello, D.; Scarpiniti, M.; Baccarelli, E.; Uncini, A.. - (2020), pp. 4167-4171. ((Intervento presentato al convegno 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 tenutosi a Barcelona, Spain [10.1109/ICASSP40776.2020.9054209]. | |
| Handle: | http://hdl.handle.net/11573/1435416 | |
| ISBN: | 978-1-5090-6631-5 | |
| Appartiene alla tipologia: | 04b Atto di convegno in volume |
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