December 4, 2025Applied SciencesOpen Access

CFNet: Achieving Practical Speedup in Lightweight CNNs via Channel-Focused Design and Cross-Channel Mixing

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HDHaipeng DuXi'an Jiaotong University

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Implication

CFNet demonstrates improved accuracy and resource efficiency in convolutional neural networks, suggesting better performance on resource-constrained devices.

Key Points

CFNet enhances GPU throughput significantly, improving efficiency in convolutional neural networks for mobile applications.
On ImageNet-1K, CFNet-A achieves 35.5% GPU throughput improvement over MobileNetV2, with accuracy gains of 1.76%.
Observational analysis on CIFAR and ImageNet reveals CFNet's practical speed advantages via novel convolutional strategies.
These advancements highlight CFNet's potential for deployment in resource-constrained devices, proving it effective for real-world applications.

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Haipeng Du (2025) studied this question.

synapsesocial.com/papers/6930dc8aea1aef094cca286a https://doi.org/10.3390/app152312620

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Implication

CFNet demonstrates improved accuracy and resource efficiency in convolutional neural networks, suggesting better performance on resource-constrained devices.

CFNet: Achieving Practical Speedup in Lightweight CNNs via Channel-Focused Design and Cross-Channel Mixing

Discussion

Member takes

Implication

Key Points

Cite This Study

CFNet: Achieving Practical Speedup in Lightweight CNNs via Channel-Focused Design and Cross-Channel Mixing

Discussion

Member takes

Implication

Key Points

Cite This Study

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