Since the spikes used by SNNs are binary (either a neuron spikes or it doesnâ€™t, there is no magnitude associated with the spikes), spikes can always be viewed as having a value of â€ś1â€ť. Since any number multiplied by â€ś1â€ť is that same number, it means that no multiplication has to be done as part of the weight update; rather, if an input neuron has spiked, then the relevant weights are simply added to the relevant neurons.

When we convert a CNN to an SNN, we turn the *rate* output of the neurons into *spiking* outputs. For example, a neuron in a CNN may have an output of 250. For the SNN, we interpret this as 250 Hz, and assuming our timestep is 1 ms (i.e. 1000 timesteps per second), that means the spiking neuron will spike once every 4 timesteps. So whereas in the CNN, we would have to multiply the value of 250 by all the weights in the following layer, in the SNN, we just have a series of spikes and so the weight updates can be done with addition.

So the overall answer is yes, NengoLoihi does produce networks that are compatible with the multiplierless design of Loihi. (That said, I donâ€™t think Loihi is completely multiplier-free. I believe that the decay in the current `u`

and voltage `v`

of a neuron involve a multiplier. But youâ€™re correct that the weight updates do not require a multiplier.)

I hope that answers your question. Let me know if anything is unclear, or if there were any parts of the question that I didnâ€™t answer or that I misunderstood.