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arXiv · 2103.13445

A Simple and Efficient Stochastic Rounding Method for Training Neural Networks in Low Precision

Lu Xia·Martijn Anthonissen·Michiel Hochstenbach·Barry Koren
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Abstract

Conventional stochastic rounding (CSR) is widely employed in the training of neural networks (NNs), showing promising training results even in low-precision computations. We introduce an improved stochastic rounding method, that is simple and efficient. The proposed method succeeds in training NNs with 16-bit fixed-point numbers and provides faster convergence and higher classification accuracy than both CSR and deterministic rounding-to-the-nearest method.

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Lu Xia, Martijn Anthonissen, Michiel Hochstenbach, Barry Koren. 2021-03-24. A Simple and Efficient Stochastic Rounding Method for Training Neural Networks in Low Precision. https://arxiv.org/abs/2103.13445

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