Overview of Memory-Efficient Architectures for Deep Learning in Real-Time Systems

With advancements in artificial intelligence (AI), deep learning (DL) has become crucial for real-time data analytics in areas like autonomous driving, healthcare, and predictive maintenance; however, its computational and memory demands often exceed the capabilities of low-end devices. This paper e...

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Vydané v:	Engineering proceedings Ročník 104; číslo 1; s. 77
Hlavní autori:	Bilgin Demir, Ervin Domazet, Daniela Mechkaroska
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	MDPI AG 01.09.2025
Predmet:	deep learning low-power devices memory-efficient architectures parameter pruning quantization real-time data processing
ISSN:	2673-4591
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