Is Integer Arithmetic Enough for Deep Learning Training?. (arXiv:2207.08822v2 [cs.LG] UPDATED)

The ever-increasing computational complexity of deep learning models makes
their training and deployment difficult on various cloud and edge platforms.
Replacing floating-point arithmetic with low-bit integer arithmetic is a
promising approach to save energy, memory footprint, and latency of deep
learning models. As such, quantization has attracted the attention of
researchers in recent years. However, using integer numbers to form a fully
functional integer training pipeline including forward pass, back-propagation,
and stochastic gradient descent is not studied in detail. Our …

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