I’ve run into some limitations on running some models on Loihi, primarily the axons limitation, synapse bit limit, and the bit loss in weight rounding. Also I get the high intercept limit when discretizing the model warning as well.
I’ve managed to overcome a good portion of the axon limitations and the synapse bit limit using the solution described here.
However I’m not sure how to handle the bit loss as it drastically changes the the expected output of various nengo models (compared to running it with the normal nengo simulator). Are there methods or suggestions to alleviate this?
Also some other questions:
- Does the high intercept limit just make the encoders have noisier responses to its original preferred directions?
- How is the synapse bits limit derived? I’ve looked through some of the code and it uses 16,384 * 64 bits, it doesn’t seem as straightforward as the axon limit (at least at first glance from the Intel Loihi slides).
- Does nengo-loihi support multiple loihi chips? If so, how does it handle placement between multiple chips? (Or is that transparent to the user?)