It's not the backprop that is holding back ML - it takes about as much time as the forward pass and requires 3x the memory. Backprop is necessary for almost all the deep neural nets that have state of the art results, attempting to replace it would push us back a decade or more.

The main problem with ML is the separation of data and compute. It takes a lot of time and energy to move data around. We need 'in memory compute'.