Deep learning is fundamentally linear algebra. Spiking networks are fundamentally event-based processors. The two concepts don’t play well together.
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.
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.