Scaling certainly is one of the next big challenges, the current network sizes severely limit us in our inference performance.
Just to clear things up: Our circuits were actually trained via backprop.
This is what allowed us to reach performance levels very close to equivalently sized but simulated SNNs (and even rather close to the accuracy of ANNs of the same size).
Fully self-learning systems are certainly one of the overarching goals of our field. Unsurprisingly, there are many challenges to be solved along the way.
> why not make an ASIC for the prettrained model
Our paper does not really touch the topic of deployment (except for the study on post-deployment degradation of the circuits, maybe). Model-specific ASICs, however, would likely not pose an economically viable solution.
> We can do robust training on GPU already.
We certainly can! Deploying those trained models on novel, "imperfect" hardware is the challenge.
Scaling certainly is one of the next big challenges, the current network sizes severely limit us in our inference performance.
Just to clear things up: Our circuits were actually trained via backprop. This is what allowed us to reach performance levels very close to equivalently sized but simulated SNNs (and even rather close to the accuracy of ANNs of the same size).