Full metadata
Title
Hardware-friendly Deep Learning for Edge Computing
Description
The Internet-of-Things (IoT) boosts the vast amount of streaming data. However, even considering the growth of the cloud computing infrastructure, IoT devices will generate two orders of magnitude more than the capacity that centralized data center servers can process or store. This trend inevitability calls for the need for offloading IoT data processing to a decentralized edge computing infrastructure. On the other hand, deep-learning-based applications gain great progress by taking advantage of heavy centralized computing resources for training large models to fit increasingly complicated tasks. Even though large-scale deep learning models perform well in terms of accuracy, their high computational complexity makes it impossible to offload them onto edge devices for real-time inference and timely response. To enable timely IoT services on edge devices, this dissertation addresses the challenge from two perspectives. On the hardware side, a new field-programmable gate array (FPGA)-based framework for binary neural network and an application-specific integrated circuit (ASIC) accelerator for natural scene text interpretation are proposed, with the awareness of the computing resources and power constraint on edge. On the algorithm side, this work presents both the methodology of building more compact models and finding better computation-accuracy trade-off for existing models.
Date Created
2021
Contributors
- Li, Yixing (Author)
- Ren, Fengbo (Thesis advisor)
- Vrudhula, Sarma (Committee member)
- Seo, Jae-Sun (Committee member)
- Li, Baoxin (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
102 pages
Language
eng
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.2.N.161275
Level of coding
minimal
Cataloging Standards
Note
Partial requirement for: Ph.D., Arizona State University, 2021
Field of study: Computer Science
System Created
- 2021-11-16 11:42:43
System Modified
- 2021-11-30 12:51:28
- 3 years ago
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