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arXiv · 1801.08621

Quantization Error as a Metric for Dynamic Precision Scaling in Neural Net Training

Ian Taras·Dylan Malone Stuart
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Abstract

Recent work has explored reduced numerical precision for parameters, activations, and gradients during neural network training as a way to reduce the computational cost of training (Na & Mukhopadhyay, 2016) (Courbariaux et al., 2014). We present a novel dynamic precision scaling (DPS) scheme. Using stochastic fixed-point rounding, a quantization-error based scaling scheme, and dynamic bit-widths during training, we achieve 98.8% test accuracy on the MNIST dataset using an average bit-width of just 16 bits for weights and 14 bits for activations, compared to the standard 32-bit floating point values used in deep learning frameworks.

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Ian Taras, Dylan Malone Stuart. 2018-01-25. Quantization Error as a Metric for Dynamic Precision Scaling in Neural Net Training. https://arxiv.org/abs/1801.08621

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