The novelty of this dissertation is the optimization and acceleration of deep neural networks aimed at real-time predictions with minimal energy consumption. It consists of cross-layer optimization, output directed dynamic quantization, and opportunistic near-data computation for deep neural network acceleration. On two datasets (CIFAR-10 and CIFAR-100), the proposed deep neural network optimization and acceleration frameworks are tested using a variety of Convolutional neural networks (e.g., LeNet-5, VGG-16, GoogLeNet, DenseNet, ResNet). Experimental results are promising when compared to other state-of-the-art deep neural network acceleration efforts in the literature.
| Identifer | oai:union.ndltd.org:unt.edu/info:ark/67531/metadc1986716 |
| Date | 08 1900 |
| Creators | Jiang, Beilei |
| Contributors | Fu, Song, Bhowmick, Sanjukta, Guo, Xuan, Yang, Qing |
| Publisher | University of North Texas |
| Source Sets | University of North Texas |
| Language | English |
| Detected Language | English |
| Type | Thesis or Dissertation |
| Format | Text |
| Rights | Public, Jiang, Beilei, Copyright, Copyright is held by the author, unless otherwise noted. All rights Reserved. |
Page generated in 0.002 seconds