norse VS MegEngine

This gives you a ludicrous advantage over current neural net accelerators. Specifically 3-5 orders is magnitude in energy and time, as demonstrated in the BranScaleS system https://www.humanbrainproject.eu/en/science-development/focu...

Unfortunately, that doesn't solve the problem of learning. Just because you can build efficient neuromorphic systems doesn't mean that we know how to train them. Briefly put, the problem is that a physical system has physical constraints. You can't just read the global state in NWN and use gradient descent as we would in deep learning. Rather, we have to somehow use local signals to approximate local behaviour that's helpful on a global scale. That's why they use Hebbian learning in the paper (what fires together, wires together), but it's tricky to get right and I haven't personally seen examples that scale to systems/problems of "interesting" sizes. This is basically the frontier of the field: we need local, but generalizable, learning rules that are stable across time and compose freely into higher-order systems.

Regarding educational material, I'm afraid I haven't seen great entries for learning about SNNs in full generality. I co-author a simulator (https://github.com/norse/norse/) based on PyTorch with a few notebook tutorials (https://github.com/norse/notebooks) that may be helpful.

I'm actually working on some open resources/course material for neuromorphic computing. So if you have any wishes/ideas, please do reach out. Like, what would a newcomer be looking for specifically?

Surrogate gradients and BPTT, this is what is implemented in Norse https://github.com/Norse/Norse. It is also possible to compute exact gradients using the Eventprop algorithm.

That repo is slightly outdated, development now continues at https://github.com/norse/norse.

The Real Performance in MegEngine

In Deep Learning you can trade space for compute by recomputing activation in backpropagation phase, known as gradient checkpointing. Classical gradient checkpointing algorithm is great but they dont work for eager execution. Dynamic Tensor Rematerialization(DTR) is a gradient checkpointing algorithm that work with eager execution, and is implemented at Megenine, a deep learning framework. Read this blogpost to learn more!