I am having trouble simulating neural ensembles greater of 1000 neurons because the memory usage is just too high when using NengoDL as backend.
I was able to use Nengo Core to simulate the same model by reducing the sampling rate of probes and setting
optimize=False for the Simulator, as suggested in the documentation. This reduced peak memory usage to a few GB.
I have tried applying the same scaling to the Probes when using NengoDL, but the memory consumption is still just too high for my machine to handle, easily running into 100 GB.
Are there any optimisations I could apply to NengoDL that will bring the memory consumption in line with that of Nengo Core, for the same model?