Great question! Sorry for the late reply. Sometimes, it is worthwhile bumping a thread if it becomes stale because it might happen to fly under the radar of mentors 
The depthwise separable convolutions reduce the number of parameters in the convolution. As such, for a small model, the model capacity may be decreased significantly if the 2D convolutions are replaced by depthwise separable convolutions. As a result, the model may become sub-optimal. However, if properly used, depthwise separable convolutions can give you efficiency without dramatically damaging your model performance.
The key take away, again, is:
The depthwise separable convolutions reduce the number of parameters in the convolution.
As an exercise, make up a toy example and calculate the number of params for each method. Prof Andrew Ng demonstrated the number of multiplications, but not the number of params. It is a good exercise and might help you understand even better.