10. Release Notes¶
Software ManifestTI_MCU_2.0.0
Supported devices
Expanded layer configs that can be supported on TI-NPU
Added an option to compress TI-NPU layer data
Optimized floating point inference on C29x core
Supported quantized models in QDQ format with integer inference code on CPU
TI_MCU_1.3.0
Windows and Linux support
Multiple model support in the same application
Generic padding support
Optimized performance for motor fault classification models and arc fault detection models
With TI-NPU acceleration, these models saw a 1.2x to 1.5x speedup from TI_MCU_1.2.0 release
With software-only execution on C28, these models saw a 3.9x to 4.8x speedup from TI_MCU_1.2.0 release
Added example and documentation on how to apply TI-NPU quantization to an existing PyTorch training script
Simplified command line options
TI_MCU_1.2.0
Supported devices
F28P55x and C28x core based devices
Supported Models
Motor fault classification
Arc fault detection