mdh.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
NeuroPower: Designing Energy Efficient Convolutional Neural Network Architecture for Embedded Systems
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
Show others and affiliations
2019 (English)In: The 28th International Conference on Artificial Neural Networks ICANN 2019, Munich, Germany: Springer , 2019Conference paper, Published paper (Refereed)
Abstract [en]

Convolutional Neural Networks (CNNs) suffer from energy-hungry implementation due to their computation and memory intensive processing patterns. This problem is even more significant by the proliferation of CNNs on embedded platforms. To overcome this problem, we offer NeuroPower as an automatic framework that designs a highly optimized and energy efficient set of CNN architectures for embedded systems. NeuroPower explores and prunes the design space to find improved set of neural architectures. Toward this aim, a multi-objective optimization strategy is integrated to solve Neural Architecture Search (NAS) problem by near-optimal tuning network hyperparameters. The main objectives of the optimization algorithm are network accuracy and number of parameters in the network. The evaluation results show the effectiveness of NeuroPower on energy consumption, compacting rate and inference time compared to other cutting-edge approaches. In comparison with the best results on CIFAR-10/CIFAR-100 datasets, a generated network by NeuroPower presents up to 2.1x/1.56x compression rate, 1.59x/3.46x speedup and 1.52x/1.82x power saving while loses 2.4%/-0.6% accuracy, respectively.

Place, publisher, year, edition, pages
Munich, Germany: Springer , 2019.
Keywords [en]
Convolutional neural networks (CNNs), Neural Architecture Search (NAS), Embedded Systems, Multi-Objective Optimization
National Category
Engineering and Technology Computer Systems
Identifiers
URN: urn:nbn:se:mdh:diva-45043OAI: oai:DiVA.org:mdh-45043DiVA, id: diva2:1345193
Conference
The 28th International Conference on Artificial Neural Networks ICANN 2019, 17 Sep 2019, Munich, Germany
Projects
DPAC - Dependable Platforms for Autonomous systems and ControlDeepMaker: Deep Learning Accelerator on Commercial Programmable DevicesAvailable from: 2019-08-23 Created: 2019-08-23 Last updated: 2019-08-23Bibliographically approved

Open Access in DiVA

No full text in DiVA

Authority records BETA

Loni, MohammadDaneshtalab, MasoudNolin, Mikael

Search in DiVA

By author/editor
Loni, MohammadSeenan, SimaDaneshtalab, MasoudNolin, Mikael
By organisation
Embedded Systems
Engineering and TechnologyComputer Systems

Search outside of DiVA

GoogleGoogle Scholar

urn-nbn

Altmetric score

urn-nbn
Total: 29 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf