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Titel:

Variant Parallelism: Lightweight Deep Convolutional Models for Distributed Inference on IoT Devices

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
Asadi, Navidreza; Goudarzi, Maziar
Abstract:
Two major techniques are commonly used to meet real-time inference limitations when distributing models across resource-constrained IoT devices: 1) model parallelism (MP) and 2) class parallelism (CP). In MP, transmitting bulky intermediate data (orders of magnitude larger than input) between devices imposes huge communication overhead. Although CP solves this problem, it has limitations on the number of submodels. In addition, both solutions are fault intolerant, an issue when deployed on edge...     »
Zeitschriftentitel:
IEEE Internet of Things Journal
Jahr:
2024
Band / Volume:
11
Heft / Issue:
1
Seitenangaben Beitrag:
345-352
Volltext / DOI:
doi:10.1109/jiot.2023.3285877
Verlag / Institution:
Institute of Electrical and Electronics Engineers (IEEE)
E-ISSN:
2327-46622372-2541
Publikationsdatum:
01.01.2024
Semester:
WS 23-24
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