Pregled bibliografske jedinice broj: 1191616
Emotions on Edge - the Dependence of Different Characteristics of the Convolutional Neural Network on the Number of Classes
Emotions on Edge - the Dependence of Different Characteristics of the Convolutional Neural Network on the Number of Classes // International Conference on Computer Science and Software Engineering (CSASE) - Proceedings
Duhok: Institute of Electrical and Electronics Engineers (IEEE), 2022. str. 224-229 doi:10.1109/CSASE51777.2022.9759761 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 1191616 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Emotions on Edge - the Dependence of Different
Characteristics of the Convolutional Neural
Network on
the Number of Classes
Autori
Đokić, Kristian ; Mandušić, Dubravka ; Blašković, Lucija
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
International Conference on Computer Science and Software Engineering (CSASE) - Proceedings
/ - Duhok : Institute of Electrical and Electronics Engineers (IEEE), 2022, 224-229
Skup
International Conference on Computer Science and Software Engineering CSASE 2022
Mjesto i datum
Duhok, Irak, 15.03.2022. - 17.03.2022
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
SoC , microcontroller , CNN , IoT , machine vision
Sažetak
Machine learning is most often associated with powerful computers, but lately, it is increasingly present on both microcontrollers and systems on a chip. Initially, simple machine learning algorithms were used on these platforms, but today we already see high-resolution cameras and convolutional neural networks used to process camera data. Models for such systems are prepared on large and powerful computers, but previously trained neural networks could be copied and used on microcontrollers and systems on a chip. This paper aims to measure the performance and characteristics of systems on a chip in which a convolution neural network for recognizing facial emotions is implemented. A different number of emotions are used to determine the impact of this change on performance, and the effect of quantization on performance and model size is also analyzed.
Izvorni jezik
Engleski
Znanstvena područja
Informacijske i komunikacijske znanosti
POVEZANOST RADA
Ustanove:
Veleučilište u Požegi
Citiraj ovu publikaciju:
Časopis indeksira:
- Web of Science Core Collection (WoSCC)
- Emerging Sources Citation Index (ESCI)