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Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation

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arxiv 2004.09602 v1 pith:4CAQC7FG submitted 2020-04-20 cs.LG stat.ML

Hao Wu , Patrick Judd , Xiaojie Zhang , Mikhail Isaev , Paulius Micikevicius This is my paper · ORCID
classification cs.LGstat.ML
keywords quantizationintegerdeepincludinginferencemodelsnetworksneural
verification ladder T0 review T1 audit T2 compute T3 formal
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Quantization techniques can reduce the size of Deep Neural Networks and improve inference latency and throughput by taking advantage of high throughput integer instructions. In this paper we review the mathematical aspects of quantization parameters and evaluate their choices on a wide range of neural network models for different application domains, including vision, speech, and language. We focus on quantization techniques that are amenable to acceleration by processors with high-throughput integer math pipelines. We also present a workflow for 8-bit quantization that is able to maintain accuracy within 1% of the floating-point baseline on all networks studied, including models that are more difficult to quantize, such as MobileNets and BERT-large.

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Cited by 13 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 218 citations worldwide. Full citation record

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