An empirical study of TensorFlow lite performances in Raspberry Pi / Mohamad Harith Mohd Zahid

Mohd Zahid, Mohamad Harith (2020) An empirical study of TensorFlow lite performances in Raspberry Pi / Mohamad Harith Mohd Zahid. [Student Project] (Unpublished)

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Abstract

Artificial intelligence today is essentially a machine that can do typically any specific task before mimicry to human intelligence. Its emphasis on a machine that thinks as it learns and experience without the help of human interaction. However, this type of learning is highly time-consuming and costly as very complex algorithms are present. Therefore, deep learning has been introduced and it has shown miraculous successes in the machine learning technique among a variety of functions. The popularity of its outcomes has open to several fields of studies with the help of deep learning open-source software tools in high powered devices. This research is proposed to predetermine the best suitable comparison of an empirical study of TensorFlow lite performances in Raspberry Pi. Therefore, with the use of deep learning open-source tools land pre-existed models from KERAS. The outcome of the benchmark is based on the throughput, energy, latency, memory footprint, and framerate per second in a low powered GPU device. Thus, it can be concluded that as the processing data increase the time average decrease but vice versa to the different types of models where it increases with the more data it processes over time.

Metadata

Item Type: Student Project
Creators:
Creators
Email
Mohd Zahid, Mohamad Harith
2016263814
Contributors:
Contribution
Name
Email / ID Num.
Thesis advisor
Fitri Marzuki, Mohd Ikmal
UNSPECIFIED
Subjects: T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Electronics
T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Electronics > Information display systems
T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Electronics > Computer engineering. Computer hardware
T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Electronics > Computer engineering. Computer hardware > Malaysia
Divisions: Universiti Teknologi MARA, Pulau Pinang > Permatang Pauh Campus > Faculty of Electrical Engineering
Programme: Bachelor of Engineering (Hons) Electrical And Electronic Engineering
Item ID: 44322
Uncontrolled Keywords: Artificial Intelligence, Raspberry Pi, Energy
URI: https://ir.uitm.edu.my/id/eprint/44322

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