  We explore calibration properties at various precisions for three
architectures: ShuffleNetv2, GhostNet-VGG, and MobileOne; and two datasets:
CIFAR-100 and PathMNIST. The quality of calibration is observed to track the
quantization quality; it is well-documented that performance worsens with lower
precision, and we observe a similar correlation with poorer calibration. This
becomes especially egregious at 4-bit activation regime. GhostNet-VGG is shown
to be the most robust to overall performance drop at lower precision. We find
that temperature scaling can improve calibration error for quantized networks,
with some caveats. We hope that these preliminary insights can lead to more
opportunities for explainable and reliable EdgeML.
