Tensorflow Lite, Edge Computing, Model Quantization, Energy Disaggregation.
and outputs are kept in float. Advantages: Provides a balance between model size and compatibility, allowing for int8 quantization where possible and falling back to float for unsupported operations. Usage Scenario: Useful when full int8 quantization leads to compatibility issues or significant