Release Notes 5.9
This section lists the new functionality, improvements and some of the major changes related to DEEPCRAFT™ Model Converter.
New Features and Enhancements
Multiple Input and Output Model support for .h5, .keras and .tflite
- Generate code for models with multiple inputs and outputs
- Use the NPZ data format for multiple input and output models
- Deploy multiple input and output models without modifying the original model architecture
Multiple input and output support for PyTorch models will be added in an upcoming release.
Int16x8 Quantization is supported for Pytorch Models
You can quantize PyTorch models exported as .pt2 to INT16×8 by providing representative input samples for calibration. Achieve higher accuracy with a small increase in memory usage and inference time for models that benefit from larger activation data types.
Tensorflow .keras model support
DEEPCRAFT™ Model Converter now supports models built with TensorFlow 2 and Keras 3 through the .keras model file format.
Support for random calibration and validation data generation
The support for random calibration and validation data generation lets you deploy models and measure on-device inference time without preparing a dataset.
Generate code for PSOC™ Edge M55
You can generate code to deploy models on PSOC™ Edge M55 standalone. Models can be deployed to PSOC Edge E81 and E82 without the U55 NPU.
Fixes
- Automatic reading of input shape for pytorch models
- Other small bug fixes and improvements