Stochastic Layer-Wise Precision in Deep Neural Networks

Griffin Lacey, Graham W. Taylor, Shawki Areibi
Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:662-671, 2018.

Abstract

Low precision weights, activations, and gradients have been proposed as a way to improve the computational efficiency and memory footprint of deep neural networks. Recently, low precision networks have even shown to be more robust to adversarial attacks. However, typical implementations of low precision DNNs use uniform precision across all layers of the network. In this work, we explore whether a heterogeneous allocation of precision across a network leads to improved performance, and introduce a learning scheme where a DNN stochastically explores multiple precision configurations through learning. This permits a network to learn an optimal precision configuration. We show on convolutional neural networks trained on MNIST and ILSVRC12 that even though these nets learn a uniform or near-uniform allocation strategy respectively, stochastic precision leads to a favourable regularization effect improving generalization.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR16-lacey18a, title = {Stochastic Layer-Wise Precision in Deep Neural Networks}, author = {Lacey, Griffin and Taylor, Graham W. and Areibi, Shawki}, booktitle = {Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence}, pages = {662--671}, year = {2018}, editor = {Globerson, Amir and Silva, Ricardo}, volume = {R16}, series = {Proceedings of Machine Learning Research}, month = {06--10 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r16/main/assets/lacey18a/lacey18a.pdf}, url = {https://proceedings.mlr.press/r16/lacey18a.html}, abstract = {Low precision weights, activations, and gradients have been proposed as a way to improve the computational efficiency and memory footprint of deep neural networks. Recently, low precision networks have even shown to be more robust to adversarial attacks. However, typical implementations of low precision DNNs use uniform precision across all layers of the network. In this work, we explore whether a heterogeneous allocation of precision across a network leads to improved performance, and introduce a learning scheme where a DNN stochastically explores multiple precision configurations through learning. This permits a network to learn an optimal precision configuration. We show on convolutional neural networks trained on MNIST and ILSVRC12 that even though these nets learn a uniform or near-uniform allocation strategy respectively, stochastic precision leads to a favourable regularization effect improving generalization.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Stochastic Layer-Wise Precision in Deep Neural Networks %A Griffin Lacey %A Graham W. Taylor %A Shawki Areibi %B Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2018 %E Amir Globerson %E Ricardo Silva %F pmlr-vR16-lacey18a %I PMLR %P 662--671 %U https://proceedings.mlr.press/r16/lacey18a.html %V R16 %X Low precision weights, activations, and gradients have been proposed as a way to improve the computational efficiency and memory footprint of deep neural networks. Recently, low precision networks have even shown to be more robust to adversarial attacks. However, typical implementations of low precision DNNs use uniform precision across all layers of the network. In this work, we explore whether a heterogeneous allocation of precision across a network leads to improved performance, and introduce a learning scheme where a DNN stochastically explores multiple precision configurations through learning. This permits a network to learn an optimal precision configuration. We show on convolutional neural networks trained on MNIST and ILSVRC12 that even though these nets learn a uniform or near-uniform allocation strategy respectively, stochastic precision leads to a favourable regularization effect improving generalization. %Z Reissued by PMLR on 04 October 2026.
APA
Lacey, G., Taylor, G.W. & Areibi, S.. (2018). Stochastic Layer-Wise Precision in Deep Neural Networks. Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R16:662-671 Available from https://proceedings.mlr.press/r16/lacey18a.html. Reissued by PMLR on 04 October 2026.

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