Mobiprox : supporting dynamic approximate computing on mobiles

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

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.

• Jezik: Angleški jezik
• Ključne besede: approximate computing, context-awareness, mobile deep learning, DL, ubiquitous computing
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FRI - Fakulteta za računalništvo in informatiko
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2024
• Št. strani: Str. 16873-16886
• Številčenje: Vol. 11, no. 9
• PID: 20.500.12556/RUL-154705
• UDK: 004
• ISSN pri članku: 2327-4662
• DOI: 10.1109/JIOT.2024.3365957
• COBISS.SI-ID: 186557955
• Datum objave v RUL: 23.02.2024

Gradivo je del revije

• Naslov: IEEE internet of things journal
• Skrajšan naslov: IEEE internet things j.
• Založnik: Institute of Electrical and Electronics Engineers
• ISSN: 2327-4662
• COBISS.SI-ID: 525375257

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: približno računanje, kontekstno zavedanje, mobilno globoko učenje, vseprisotno računalništvo