not sure I get your point, both DNNs and SVMs require one forward pass for inference, so there is no difference.
if SVM model can converge in one epoch, how is it not less efficient than the status quo with DNNs?
Furthermore, number of parameters do not (necessarily) grow with the size of the training data, can be reused if you get more data, can be quantized/pruned/etc. There's not really an easy way to do these things with SVMs as far as I understand.