Short answer: yes. Basically there are two options for future ReRAM (memristive) learning system: in-situ/on-chip learning , in which all learning rules are locally derived and enforced, and ex-situ learning systems in which we do what you suggested- import weights from more computationally/power expensive substrates. there is probably abundant promise in both approaches moving forward. I recommend looking at some recent papers by the Strukov group [1] as well as my own [2] to see the limitations of these approaches. Strukov paper skirts around the issue to a certain degree but they admit in supplementary material scaling issues are not favorable with their approach. our work takes the 'neural FPGA' approach quite literally. But, their approach may , with some improvements , do rather well for an on-chip backprop implementation. Let's see what they do next. Lastly, as far as hybrid approaches, there is a recent IBM paper which is really nice which talks about deep neural net acceleration with ReRAM. If you're really curious let me know and I"ll try to dig it up. [1] http://www.nature.com/nature/journal/v521/n7550/abs/nature14... [2]http://www.nature.com/articles/srep31932