ShadowNet: A Secure and Efficient On-device Model Inference System for Convolutional Neural Networks
Summary
ShadowNet: A Secure and Efficient On-device Model Inference System for Convolutional Neural Networks is a scholarly article[1].
Key Facts
ShadowNet: A Secure and Efficient On-device Model Inference System for Convolutional Neural Networks's instance of is recorded as scholarly article[2].
References
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Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.
APA4ort.xyz Knowledge Graph. (2026). ShadowNet: A Secure and Efficient On-device Model Inference System for Convolutional Neural Networks. Retrieved May 24, 2026, from https://4ort.xyz/entity/shadownet-a-secure-and-efficient-on-device-model-inference-system-for-convolutional-neural-networks
MLA“ShadowNet: A Secure and Efficient On-device Model Inference System for Convolutional Neural Networks.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/shadownet-a-secure-and-efficient-on-device-model-inference-system-for-convolutional-neural-networks.
BibTeX@misc{4ortxyz_shadownet-a-secure-and-efficient-on-device-model-inference-system-for-convolutional-neural-networks_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{ShadowNet: A Secure and Efficient On-device Model Inference System for Convolutional Neural Networks}}, year = {2026}, url = {https://4ort.xyz/entity/shadownet-a-secure-and-efficient-on-device-model-inference-system-for-convolutional-neural-networks}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): ShadowNet: A Secure and Efficient On-device Model Inference System for Convolutional Neural Networks — https://4ort.xyz/entity/shadownet-a-secure-and-efficient-on-device-model-inference-system-for-convolutional-neural-networks (retrieved 2026-05-24)
