On-Device Split Inference for Edge Devices

A literature review

Bachelor Thesis (2024)
Author(s)

B. Kozan (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Contributor(s)

Q. Wang – Mentor (TU Delft - Embedded Systems)

Mingkun Yang – Mentor (TU Delft - Embedded Systems)

R. Zhu – Mentor (TU Delft - Embedded Systems)

JA Pouwelse – Graduation committee member (TU Delft - Data-Intensive Systems)

Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
More Info
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Publication Year
2024
Language
English
Graduation Date
25-06-2024
Awarding Institution
Delft University of Technology
Project
CSE3000 Research Project
Programme
Computer Science and Engineering
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
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Abstract

Nowadays, the popularity of machine learning and artificial intelligence algorithms is very high. A new research direction has emerged where the machine learning algorithms are executed on resource-constrained embedded devices. With the development of the Internet of Things paradigm, these edge devices are deployed in a lot of places. Due to the limited resources of embedded devices, it is difficult to bring machine learning algorithms to them. This is where the on-device split inference comes in. It is possible to distribute the inference between multiple edge devices and the cloud so that the edge devices can execute the inference of complex machine learning models. This paper presents a systematized literature review of papers that focus on on-device split inference for edge devices. The papers are analyzed and compared based on pre-determined questions and displayed based on several categories.

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