Analytical Guarantees on Numerical Precision of Deep Neural Networks


Charbel Sakr, Yongjune Kim, Naresh Shanbhag ;
Proceedings of the 34th International Conference on Machine Learning, PMLR 70:3007-3016, 2017.


The acclaimed successes of neural networks often overshadow their tremendous complexity. We focus on numerical precision – a key parameter defining the complexity of neural networks. First, we present theoretical bounds on the accuracy in presence of limited precision. Interestingly, these bounds can be computed via the back-propagation algorithm. Hence, by combining our theoretical analysis and the back-propagation algorithm, we are able to readily determine the minimum precision needed to preserve accuracy without having to resort to time-consuming fixed-point simulations. We provide numerical evidence showing how our approach allows us to maintain high accuracy but with lower complexity than state-of-the-art binary networks.

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