  Deep learning has made significant improvements at many image processing
tasks in recent years, such as image classification, object recognition and
object detection. Convolutional neural networks (CNN), which is a popular deep
learning architecture designed to process data in multiple array form, show
great success to almost all detection \& recognition problems and computer
vision tasks. However, the number of parameters in a CNN is too high such that
the computers require more energy and larger memory size. In order to solve
this problem, we propose a novel energy efficient model Binary Weight and
Hadamard-transformed Image Network (BWHIN), which is a combination of Binary
Weight Network (BWN) and Hadamard-transformed Image Network (HIN). It is
observed that energy efficiency is achieved with a slight sacrifice at
classification accuracy. Among all energy efficient networks, our novel
ensemble model outperforms other energy efficient models.
