Event-based backpropagation for exact gradients in spiking neural networks

Re:Event-based backpropagation for exact gradients in spikin... by periheli0n on Wednesday June 02, 2021 @09:38AM (#27368526)

But is sparse activation sufficient to motivate the use of SNNs? In my opinion one needs a temporal component as well.

Sparse activations that don't also have a time component (i.e. are sparse in space and time) can be very well implemented without events.

Granted, SNN processors can handle sparse activations better than matrix accelerators. But then again, SNN accelerators might carry lots of SNN overhead that is not required for sparse activations alone.

Edit: A good example for a non-spiking sparse activation accelerator is the NullHop architecture [1].

[1] https://ieeexplore.ieee.org/abstract/document/8421093

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Re:Event-based backpropagation for exact gradients in spikin... by FrereKhan on Thursday June 03, 2021 @10:58AM (#27382338)

I agree. The use case needs to justify having state, otherwise the ideal architecture is something like NullHop. Temporal signal processing / vision processing tasks are ideal for SNNs, especially if the inputs can also be sparse.

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