Many researchers have been trying hard to shoe-horn deep ANNs into spiking networks for the last 10 years. But this doesn’t change the fact that linear algebra is best accelerated by linear algebra accelerators (i.e. GPUs/TPUs).
Generally, spiking networks will likely have an edge when the signals they are processing are events in time. For example, when processing signal streams from event based sensors, like silicon retinas. There’s also evidence that event-based control has advantages over their periodically-sampling equivalents.
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].
However, I think the MNIST and the Ying/Yang dataset, using latency-coding, are not the ideal example to demonstrate its performance.
These datasets are useful to demonstrate nonlinear classification, and it's certainly great to see that the spiking network performs competitively. However, the transformation into a latency code costs time, in terms of computation, and also in terms of representation, before even one item is classified. Perceptron-based ANNs with continuous outputs don't require this step and will always have an edge over spiking networks in such scenarios.
I think what the field is really lacking is an ML problem that can leverage spiking networks directly, that does not require costly conversion of data into a representation that is suitable for spiking networks.
