Low-cost Design and Implementation for Binary Convolutional Neural Networks

• Main Authors: TANG, CHI-HUAN, 唐其煥
• Other Authors: LIEN,CHIH-YUAN
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/c5aa76

碩士 === 國立高雄應用科技大學 === 電子工程系 === 106 === In recent years, deep learning has been one of the most popular subject in academia and widely used in many fields such as computer vision, image classification, motion recognition, voice recognition, and big-data analysis tasks. Although the larger neural network architecture can improve accuracy obviously, the cost of memory usage, power consumption and time consumption also increase. How to use memory and speed effectively to achieve a certain accuracy has been the most popular subject in recent years. In the first part of this thesis, we will introduce the development of convolution neural network in recent years, and then we will introduce and explore the diversification of binary neural network. Finally, we will focus on Deep Residual Network and propose our method to improved XNOR-Net. By adjusting Deep Residual Network basic structure, increasing the possible of input layer and replacing more simply bit counter than multiplier, we can simplify large network architecture and increase accuracy than previous network greatly. The experimental results demonstrate that our design achieves the same performances in memory usage as XNOR-Net. Moreover, it can dramatically increase accuracy in Cifar10/Cifar100 datasets, and achieve the good accuracy result than other binary neural network paper.