Base Model Adaptation Across Device-Specific ML Constraints

The patent applies universality by creating a single base machine learning model that can be adapted to multiple device types through parameter adjustment rather than training separate models. The base model serves multiple functions across different device architectures (GPU, CPU, FPGA, edge computers) by modifying execution parameters such as batch size, learning rate, and computational precision based on target device capabilities.

The patent implements parameter changes by systematically adjusting model parameters (batch size, learning rate, precision, memory allocation) based on the computational characteristics of different device types. This allows the same base model to be efficiently deployed across devices with varying computing power, memory capacity, and processing architectures without requiring separate training processes.

The patent applies preliminary action by pre-training a single base model on high-capacity devices (such as GPUs) that can handle the computational burden of training. Once the base model is trained, it is then adapted to other device types through parameter modification rather than retraining, thereby concentrating computational resources in the initial training phase and avoiding redundant training computations for each device type.

The patent implements copying by creating device-specific model versions through parameter adjustment and configuration copying from the base model rather than creating entirely new models. The base model serves as a template that is copied and adapted to different device architectures, significantly reducing the computational resources required compared to training separate models from scratch for each device type.

The patent applies segmentation by separating model development into two distinct phases: (1) base model training on high-capacity devices, and (2) device-specific parameter adaptation. This segmentation allows the complex training process to be performed once on powerful hardware, while subsequent device adaptations involve only parameter adjustments, thereby reducing overall development complexity while maintaining execution efficiency across different device types.

The patent implements dynamics by making model parameters adjustable and adaptable based on target device characteristics. Rather than creating static, device-specific models, the system dynamically adjusts parameters such as batch size, precision, and memory allocation based on the computational capabilities of the target device, allowing a single base model to efficiently serve multiple device types with varying performance characteristics.