Self-adaptive approximate mobile deep learning

Knez, Timotej (avtor), Machidon, Octavian-Mihai (avtor), Pejović, Veljko (avtor)

Edge intelligence is currently facing several important challenges hindering its performance, with the major drawback being meeting the high resource requirements of deep learning by the resource-constrained edge computing devices. The most recent adaptive neural network compression techniques demonstrated, in theory, the potential to facilitate the flexible deployment of deep learning models in real-world applications. However, their actual suitability and performance in ubiquitous or edge computing applications has not, to this date, been evaluated. In this context, our work aims to bridge the gap between the theoretical resource savings promised by such approaches and the requirements of a real-world mobile application by introducing algorithms that dynamically guide the compression rate of a neural network according to the continuously changing context in which the mobile computation is taking place. Through an in-depth trace-based investigation, we confirm the feasibility of our adaptation algorithms in offering a scalable trade-off between the inference accuracy and resource usage. We then implement our approach on real-world edge devices and, through a human activity recognition application, confirm that it offers efficient neural network compression adaptation in highly dynamic environments. The results of our experiment with 21 participants show that, compared to using static network compression, our approach uses 2.18× less energy with only a 1.5% drop in the average accuracy of the classification.

• Jezik: Angleški jezik
• Ključne besede: mobile sensing, neural networks, dynamic optimization, quantization, DNN slimming
• 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: 2021
• Št. strani: 23 str.
• Številčenje: Vol. 10, iss. 23, art. 2958
• PID: 20.500.12556/RUL-136700
• UDK: 004
• ISSN pri članku: 2079-9292
• DOI: 10.3390/electronics10232958
• COBISS.SI-ID: 89465859
• Datum objave v RUL: 16.05.2022

Gradivo je del revije

• Naslov: Electronics
• Skrajšan naslov: Electronics
• Založnik: MDPI
• ISSN: 2079-9292
• COBISS.SI-ID: 523068953

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.
• Začetek licenciranja: 01.12.2021

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: mobilno zaznavanje, nevronske mreže, dinamična optimizacija, kvantizacija, tanjšanje globokih mrež