  Automotive Cyber-Physical Systems (ACPS) have attracted a significant amount
of interest in the past few decades, while one of the most critical operations
in these systems is the perception of the environment. Deep learning and,
especially, the use of Deep Neural Networks (DNNs) provides impressive results
in analyzing and understanding complex and dynamic scenes from visual data. The
prediction horizons for those perception systems are very short and inference
must often be performed in real time, stressing the need of transforming the
original large pre-trained networks into new smaller models, by utilizing Model
Compression and Acceleration (MCA) techniques. Our goal in this work is to
investigate best practices for appropriately applying novel weight sharing
techniques, optimizing the available variables and the training procedures
towards the significant acceleration of widely adopted DNNs. Extensive
evaluation studies carried out using various state-of-the-art DNN models in
object detection and tracking experiments, provide details about the type of
errors that manifest after the application of weight sharing techniques,
resulting in significant acceleration gains with negligible accuracy losses.
