Mobiprox : supporting dynamic approximate computing on mobiles

Fabjančič, Matevž (author), Machidon, Octavian-Mihai (author), Sharif, Hashim (author), Zhao, Yifan (author), Misailović, Saša (author), Pejović, Veljko (author)

Runtime-tunable context-dependent network compression would make mobile deep learning (DL) adaptable to often varying resource availability, input “difficulty”, or user needs. The existing compression techniques significantly reduce the memory, processing, and energy tax of DL, yet, the resulting models tend to be permanently impaired, sacrificing the inference power for reduced resource usage. The existing tunable compression approaches, on the other hand, require expensive re-training, do not support arbitrary strategies for adapting the compression and do not provide mobile-ready implementations. In this paper we present Mobiprox, a framework enabling mobile DL with flexible precision. Mobiprox implements tunable approximations of tensor operations and enables runtime-adaptable approximation of the individual network layers. A profiler and a tuner included with Mobiprox identify the most promising neural network approximation configurations leading to the desired inference quality with the minimal use of resources. Furthermore, we develop control strategies that depending on contextual factors, such as the input data difficulty, dynamically adjust the approximation levels across a mobile DL model’s layers. We implement Mobiprox in Android OS and through experiments in diverse mobile domains, including human activity recognition and spoken keyword detection, demonstrate that it can save up to 15% system-wide energy with a minimal impact on the inference accuracy.

• Language: English
• Keywords: approximate computing, context-awareness, mobile deep learning, ubiquitous computing
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FRI - Faculty of Computer and Information Science
• Publication status: Published
• Publication version: Version of Record
• Publication date: 01.01.2024
• Year: 2024
• Number of pages: 14 str.
• Numbering: Vol. , no.
• PID: 20.500.12556/RUL-154705
• UDC: 004
• ISSN on article: 2327-4662
• DOI: 10.1109/JIOT.2024.3365957
• COBISS.SI-ID: 186557955
• Publication date in RUL: 23.02.2024

Record is a part of a journal

• Title: IEEE internet of things journal
• Shortened title: IEEE internet things j.
• Publisher: Institute of Electrical and Electronics Engineers
• ISSN: 2327-4662
• COBISS.SI-ID: 525375257

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

• Language: Slovenian
• Keywords: približno računanje, kontekstno zavedanje, mobilno globoko učenje, vseprisotno računalništvo