Release Note 1.28.0

ONE Runtime

Support on-device training with circle model
- Training parameter can be passed to onert via onert`s experimental API or loading new model format including training information: circle_plus
- Trained model can be exported to circle model via experimental API nnfw_train_export_circle
- Supporting Transfer learning from a pre-trained circle model
Introduce circle_plus_gen tool
- Generates a circle_plus model file with a given training hyperparameters
- Shows a _circle_plus model details

onert supports runtime configuration API for prepare and execution phase via experimental APIs
- nnfw_set_prepare_config sets configuration for prepare phase, and nnfw_reset_prepare_config resets it to default value
- nnfw_set_execution_config sets configuration for execution phase, and nnfw_reset_execution_config resets it to default value
- Supporting prepare phase configuration: prepare execution time profile
- Supporting execution phase configuration: dump minmax data, dump execution trace, dump execution time
Introduce new API to set onert workspace directory: nnfw_set_workspace
- onert workspace directory is used to store intermediate files during prepare and execution phase

onert supports full quantization of uint8/int16 type weight and activation.
- To quantize activation, onert requires minmax data of activation.
onert supports on-device code generation for special backend requiring special binary format such as DSP, NPU.
- Introduce new experimental API for code generation: nnfw_codegen

If loaded model is quantized model, onert allows float type I/O buffer
- onert converts float type input buffer to quantized type internally
- onert fills float type output buffers by converting quantized type output data to float type internally
On multimodel package, onert allows edges between quantized model and float type model