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NeuroPower: Designing Energy Efficient Convolutional Neural Network Architecture for Embedded Systems

Loni, M.; Zoljodi, A.; Sinaei, S.; Daneshtalab, M.; Sjödin, M. (2019). NeuroPower: Designing Energy Efficient Convolutional Neural Network Architecture for Embedded Systems. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (208−222). Springer Verlagservice@springer.de.10.1007/978-3-030-30487-4_17.
kogumikuartikkel/peatükk raamatus/kogumikus
Loni, M.; Zoljodi, A.; Sinaei, S.; Daneshtalab, M.; Sjödin, M.
  • Inglise
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Journal of Functional Foods
0302-9743
9783030304867
11727 LNCS
2019
208222
Ilmunud
1.1. Teadusartiklid, mis on kajastatud Web of Science andmebaasides Science Citation Index Expanded, Social Sciences Citation Index, Arts & Humanities Citation Index, Emerging Sources Citation Index ja/või andmebaasis Scopus (v.a. kogumikud)
Teadmata
SCOPUS

Viited terviktekstile

dx.doi.org/10.1007/978-3-030-30487-4_17

Seotud asutused

Shiraz University of Technology; Mälardalens högskola

Lisainfo

© 2019, Springer Nature Switzerland AG. 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.
Convolutional Neural Networks (CNNs) | Embedded systems | Multi-objective optimization | Neural Architecture Search (NAS)