Show/Hide Menu
Hide/Show Apps
anonymousUser
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Açık Bilim Politikası
Açık Bilim Politikası
Frequently Asked Questions
Frequently Asked Questions
Browse
Browse
By Issue Date
By Issue Date
Authors
Authors
Titles
Titles
Subjects
Subjects
Communities & Collections
Communities & Collections
Effect of quantization on the performance of deep networks
Download
index.pdf
Date
2020
Author
Kütükcü, Başar
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
5
views
5
downloads
Deep neural networks performed greatly for many engineering problems in recent years. However, power and memory hungry nature of deep learning algorithm prevents mobile devices to benefit from the success of deep neural networks. The increasing number of mobile devices creates a push to make deep network deployment possible for resource-constrained devices. Quantization is a solution for this problem. In this thesis, different quantization techniques and their effects on deep networks are examined. The techniques are benchmarked by their success and memory requirements. The effects of quantization are examined for different network architectures including shallow, overparameterized, deep, residual, efficient models. Architecture specific problems are observed and related solutions are proposed. Quantized models are compared with ground-up efficiently designed models. The advantages and disadvantages of each technique are examined. Standard and quantized convolution operations implemented in real systems ranging from low power embedded systems to powerful desktop computer systems. Computation time and memory requirements are examined in these real systems.
Subject Keywords
Neural networks.
,
Keywords: Deep Neural Networks
,
Quantization
URI
http://etd.lib.metu.edu.tr/upload/12625535/index.pdf
https://hdl.handle.net/11511/45772
Collections
Graduate School of Natural and Applied Sciences, Thesis