DeepShare: Sharing ReLU Across Channels and Layers for Efficient Private Inference

The article likely presents a novel method, DeepShare, to optimize private inference by sharing ReLU activations. This suggests a focus on improving efficiency and potentially reducing computational costs or latency in privacy-preserving machine learning scenarios. The use of ReLU sharing across channels and layers indicates a strategy to reduce the overall complexity of the model or the operations performed during inference.

• •Focus on efficient private inference.
• •Utilizes ReLU sharing across channels and layers.
• •Potential for reduced computational costs or latency.