On the other hand, most of the latency is in the last few layers, and probably there isn't as much to be saved there.
The biggest problem might be that the bottom layer will anyway have to store full-width numbers, since we must be sure to have the low-order bits somewhere in case they weren't covered yet in earlier layers. Or we could have variable width encoding per node maybe (instead of per layer) but that does sound a bit iffy on the branch predictor.
In the end I guess I kinda like the simplicity and hence reliability of the 'just do the full tree and the first layers are cheap anyway' approach. Probably another factor 2 speedup is possible on specific datasets, but anything beyond this may not be reliably good on worst case inputs (like is the issue for the prefix partitioning methods).