  For many applications, utilizing DNNs (Deep Neural Networks) requires their
implementation on a target architecture in an optimized manner concerning
energy consumption, memory requirement, throughput, etc. DNN compression is
used to reduce the memory footprint and complexity of a DNN before its
deployment on hardware. Recent efforts to understand and explain AI (Artificial
Intelligence) methods have led to a new research area, termed as explainable
AI. Explainable AI methods allow us to understand better the inner working of
DNNs, such as the importance of different neurons and features. The concepts
from explainable AI provide an opportunity to improve DNN compression methods
such as quantization and pruning in several ways that have not been
sufficiently explored so far. In this paper, we utilize explainable AI methods:
mainly DeepLIFT method. We use these methods for (1) pruning of DNNs; this
includes structured and unstructured pruning of \ac{CNN} filters pruning as
well as pruning weights of fully connected layers, (2) non-uniform quantization
of DNN weights using clustering algorithm; this is also referred to as Weight
Sharing, and (3) integer-based mixed-precision quantization; this is where each
layer of a DNN may use a different number of integer bits. We use typical image
classification datasets with common deep learning image classification models
for evaluation. In all these three cases, we demonstrate significant
improvements as well as new insights and opportunities from the use of
explainable AI in DNN compression.
