  Deep convolutional neural network (DCNN) has achieved remarkable performance
on object detection and speech recognition in recent years. However, the
excellent performance of a DCNN incurs high computational complexity and large
memory requirement. In this paper, an equal distance nonuniform quantization
(ENQ) scheme and a K-means clustering nonuniform quantization (KNQ) scheme are
proposed to reduce the required memory storage when low complexity hardware or
software implementations are considered. For the VGG-16 and the AlexNet, the
proposed nonuniform quantization schemes reduce the number of required memory
storage by approximately 50\% while achieving almost the same or even better
classification accuracy compared to the state-of-the-art quantization method.
Compared to the ENQ scheme, the proposed KNQ scheme provides a better tradeoff
when higher accuracy is required.
