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Hybrid Fixed-Point/Binary Deep Neural Network Design Methodology for Low-Power Object Detection

Research article (IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 2020) · cited 12× · AI/ML
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Hybrid Fixed-Point/Binary Deep Neural Network Design Methodology for Low-Power Object Detection

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Hybrid Fixed-Point/Binary Deep Neural Network Design Methodology for Low-Power Object Detection is a scholarly article[1].

Key Facts

  • Hybrid Fixed-Point/Binary Deep Neural Network Design Methodology for Low-Power Object Detection's instance of is recorded as scholarly article[2].

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Direct Wikidata claims

  1. [2] ↑ . wikidata.org.

Class ancestry

  1. [1] ↑ . Wikidata. wikidata.org.

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APA 4ort.xyz Knowledge Graph. (2026). Hybrid Fixed-Point/Binary Deep Neural Network Design Methodology for Low-Power Object Detection. Retrieved May 24, 2026, from https://4ort.xyz/entity/hybrid-fixed-point-binary-deep-neural-network-design-methodology-for-low-power-object-detection
MLA “Hybrid Fixed-Point/Binary Deep Neural Network Design Methodology for Low-Power Object Detection.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/hybrid-fixed-point-binary-deep-neural-network-design-methodology-for-low-power-object-detection.
BibTeX @misc{4ortxyz_hybrid-fixed-point-binary-deep-neural-network-design-methodology-for-low-power-object-detection_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Hybrid Fixed-Point/Binary Deep Neural Network Design Methodology for Low-Power Object Detection}}, year = {2026}, url = {https://4ort.xyz/entity/hybrid-fixed-point-binary-deep-neural-network-design-methodology-for-low-power-object-detection}, note = {Accessed: 2026-05-24}}
LLM prompt According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Hybrid Fixed-Point/Binary Deep Neural Network Design Methodology for Low-Power Object Detection — https://4ort.xyz/entity/hybrid-fixed-point-binary-deep-neural-network-design-methodology-for-low-power-object-detection (retrieved 2026-05-24)

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