v0.4.2

Major updates:

Calibrations changes because of RNN layers.

• The calibration data size will no longer cut to 100 automatically.
• It is no longer shuffle the data for the user.
• User must cut the calibration sample to the size (i.e. 100) and shuffle the data when needed.
• In quantisation with max-min, allow the very small number to be saturated, such as 1.00001

Support model with multiple outputs:

• currently, the output data buff naming are nnom_output_data[], nnom_output_data1[], nnom_output_data2[]...

Add RNNoise like Voice Enhancement example:

• With well documented and demo

Depthwise Conv layers are now supported depth_multiplier arguments.

• Simply use it in Keras.

Bugs fixed:

• Update which solved the issue conv2d 1*1 with strides!=1 and cmsis-nn #84.
• RNN not passing correct Q format to the next layer.
• Deleting model causes Segment Fault
• Compiler stuck at some points with multiple output model.
• DW conv and Conv cannot calculate the correct kernel range near border of the image with padding.

Minors:

• fixed hard sigmoid, fixed compiling warning of multiple outputs
• update the KWS example's MFCC C code to align with python's MFCC. Accuracy should improve a lot.
• Improve performance of local backends (DW Conv)