CascadeC in parallel

[Henrik Klagges]

It is less than obvious how to parallelize Cascade Correlation, especially
if compared to stuff like backprop (which fits easily on specialized 'kill-
them-all' machines). If one tries to decompose the CC-computation onto
neurons, it is a quick failure: It is not obvious to do any better than
training the candidate pool in parallel. This suggest that the problem
should be decomposed into another unit. I am to foggy at this time of the
working day, however, to see another clever decomposition unit that would
be homogenous enough to fit on a SIMD architecture. CC abandons the inter-
hidden-unit parallelism by training only one at a time. It also kills of
most of the feeding calculation via freezing and cacheing. So, where does
the long vector SIMD machine fit into the picture ? It can't be a unipro-
cessor-RISC-only algorithm like 'Boot Unix on the gate-level simulator'.
Of course I could do 10 complete networks in parallel, but ...

Cheers, Henrik