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Brief overview of Edge AI accelerators for energy-constrained edge
Pomsar, L.
;
Brecko, A.
;
Zolotova, I.
Angol nyelvű Konferenciaközlemény (Könyvrészlet) Tudományos
Megjelent:
IEEE [szerk.]. IEEE 20th Jubilee World Symposium on Applied Machine Intelligence and Informatics SAMI (2022): Proceedings. (2022) ISBN:9781665497046; 9781665497039
pp. 461-466
Azonosítók
MTMT: 33087078
DOI:
10.1109/SAMI54271.2022.9780669
Scopus:
85132163495
In the last several years, there has been a big boom in the field of AI, and particularly in a subset called deep learning. The models used in deep learning are often relatively computationally expensive and problematic to execute in real-time in environments with computational and energetic constraints. In order to do so, specialized hardware, so-called AI Accelerators are often utilized. The term AI Accelerators encompass a wide variety of different devices using different technologies. This work surveys a market and introduces an overview of such accelerators for a network's edge constrained by electrical energy. © 2022 IEEE.
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2026-04-12 03:22
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Hivatkozás stílusok:
IEEE
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APA
Chicago
Harvard
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