Post-Training BatchNorm Recalibration

Gil Shomron; Uri Weiser

Post-Training BatchNorm Recalibration

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arxiv 2010.05625 v1 pith:T4MISSKV submitted 2020-10-12 cs.LG

Gil Shomron , Uri Weiser This is my paper · ORCID

classification cs.LG

keywords nb-smtmodelperformancepost-trainingrecalibrationrunningsharedstatistics

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We revisit non-blocking simultaneous multithreading (NB-SMT) introduced previously by Shomron and Weiser (2020). NB-SMT trades accuracy for performance by occasionally "squeezing" more than one thread into a shared multiply-and-accumulate (MAC) unit. However, the method of accommodating more than one thread in a shared MAC unit may contribute noise to the computations, thereby changing the internal statistics of the model. We show that substantial model performance can be recouped by post-training recalibration of the batch normalization layers' running mean and running variance statistics, given the presence of NB-SMT.

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