Err... no. MLPs are fundamentally sequential algorithms (backprop weight updating). All major innovations in NN design have been to find ways of designing the architecture to fit GPU compute paradigms.

It was an innovation, in the 80s, to map image structure to weight structure that underpins CNNs. That isnt what made CNNs trainable though.. that was alexnet, and just go read the paper... its pretty upfront about how the NN architecture is designed to fit the GPU... that's the point of it