By creating specialized digital hardware processors it might be possible to gain an advantage over microprocessor-based systems. Still, it is unlikely that this will be more than an order of magnitude, since they are based on the same technology as microprocessors: The neural circuits remain to be realised with numerical solutions of differential equations. The biggest problem lies in the fundamentals of Moore's law itself: the scaling of process technology. In the current semiconductor roadmap the progress has already slowed down. The transistor density of high-performance microprocessors is likely to increase only by a factor of 25 from 2004 to 2018. A power consumption of 300 Watts is predicted for such a hypothetical chip while the on-chip operating frequency will be in the 50 GHz range.I've blogged before on consciousness and how this might relate to computers, and what a design could look like. And some more. And more.
Changing the hardware is extremely important and much more likely to become successful.
A large problem is still the coordination between things and the problems related to associative memory. That is, when we say "cat", we instantly recall associations related to the word, strongest first (black? kitten? dead mouse?).
Most explicit knowledge systems scan their entire memory base, or have otherwise explicitly defined boundaries around knowledge to hierarchically exclude certain pools of knowledge programmatically. Thus, a key pointing to some piece of information is not recognized as such and doesn't cause a specific part of memory to highlight. It's requiring a sweep of memory to see with which asset of knowledge it's associated.
In order to be successful in the future, I think it's necessary to find ways to prevent that, to directly find some location/pool/hierarchy where something is probably located, such that it finds a match or has the ability to locate it together with other like members.