  Convolutional Neural Networks (ConvNets) are trained offline using the few
available data and may therefore suffer from substantial accuracy loss when
ported on the field, where unseen input patterns received under unpredictable
external conditions can mislead the model. Test-Time Augmentation (TTA)
techniques aim to alleviate such common side effect at inference-time, first
running multiple feed-forward passes on a set of altered versions of the same
input sample, and then computing the main outcome through a consensus of the
aggregated predictions. Unfortunately, the implementation of TTA on embedded
CPUs introduces latency penalties that limit its adoption on edge applications.
To tackle this issue, we propose AdapTTA, an adaptive implementation of TTA
that controls the number of feed-forward passes dynamically, depending on the
complexity of the input. Experimental results on state-of-the-art ConvNets for
image classification deployed on a commercial ARM Cortex-A CPU demonstrate
AdapTTA reaches remarkable latency savings, from 1.49X to 2.21X, and hence a
higher frame rate compared to static TTA, still preserving the same accuracy
gain.
