Journal
IEEE INTELLIGENT SYSTEMS
Volume 35, Issue 6, Pages 50-57Publisher
IEEE COMPUTER SOC
DOI: 10.1109/MIS.2020.3011586
Keywords
Computer architecture; Feature extraction; Intelligent systems; Detectors; Object recognition; Computational modeling; Twitter
Funding
- NVI-DIA Corporation
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Image polarity detection opens new vistas in the area of pervasive computing. State-of-the-art frameworks for polarity detection often prove computationally demanding, as they rely on deep learning networks. Thus, one faces major issues when targeting their implementation on resource-constrained embedded devices. This article presents a design strategy for convolutional neural networks that can support image-polarity detection on edge devices. The outcomes of experimental sessions, involving standard benchmarks and a pair of commercial edge devices, confirm the approach suitability.
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