MoE Layer Placement in Neural Networks for Lower Training Cost

The patent applies segmentation by dividing the neural network into distinct layers and selectively integrating MoE functionality into specific layers rather than all layers. This segmentation allows the system to capture the benefits of MoE (improved model capacity and adaptability) in layers where it is most beneficial, while avoiding the computational overhead in layers where it is less critical, thus resolving the contradiction between model capacity and computational cost.

The patent applies local quality by making different layers have different structures - some layers incorporate MoE functionality while others use conventional architectures. This allows the network to have high processing capability in layers where MoE provides the most value (such as layers processing complex sensor data patterns), while maintaining faster training in layers where conventional structures suffice, thus resolving the contradiction between processing capability and training time.

The patent applies dynamics by implementing a router mechanism that dynamically selects which expert networks to activate based on the input characteristics. This allows the network to adapt its computational resources to the specific scenario at hand - activating only the necessary experts for the current input rather than always using all components of a monolithic architecture, thus resolving the contradiction between scenario coverage and computational expense during inference.