  Embedded devices with neural network accelerators offer great versatility for
their users, reducing the need to use cloud-based services. At the same time,
they introduce new security challenges in the area of hardware attacks, the
most prominent being side-channel analysis (SCA). It was shown that SCA can
recover model parameters with a high accuracy, posing a threat to entities that
wish to keep their models confidential. In this paper, we explore the
susceptibility of quantized models implemented in OpenVINO, an embedded
framework for deploying neural networks on embedded and Edge devices. We show
that it is possible to recover model parameters with high precision, allowing
the recovered model to perform very close to the original one. Our experiments
on GoogleNet v1 show only a 1% difference in the Top 1 and a 0.64% difference
in the Top 5 accuracies.
