Speeding-up CNN inference through dimensionality reduction

Computational complexity of CNNs makes their integration in embedded systems with low power consumption requirements a challenging task, which requires the joint design and adaptation of hardware and algorithms. In this paper, we propose a new general CNN compression method, allowing to reduce both the number of parameters and operations. This method is applied to a binary face detection network which is then implemented and evaluated on hardware.