  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.
