MS Thesis Presentation: “Image Classification with Energy Efficient Hadamard Neural Networks,” Tuba Ceren Deveci (EE), EE-314, 3PM January 3 (EN)

Tuba Ceren Deveci
M.S. in Electrical and Electronics Engineering
Prof. Dr. A. Enis Cetin

The seminar will be on Wednesday, January 3, 2018 at 15:00 @ EE-314

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 investigate the energy efficient network models based on CNN architecture. In addition to previously studied energy efficient models such as Binary Weight Network (BWN), we introduce novel energy efficient models. Multiplication-free Neural Network (MFNN) adapts a previously defined multiplier-less operator to CNN and uses this operator during convolution operation.

Hadamard-transformed Image Network (HIN) is a variation of BWN, but uses compressed Hadamard-transformed images as input. Binary Weight and Hadamard-transformed Image Network (BWHIN) is developed by combining BWN and HIN as a new energy efficient model. Performances of the neural networks with different parameters and different CNN architectures are compared and analyzed on MNIST and CIFAR-10 datasets. 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.